Method and system for translating information with a higher probability of a correct translation

Information

  • Patent Grant
  • 8977536
  • Patent Number
    8,977,536
  • Date Filed
    Tuesday, June 3, 2008
    16 years ago
  • Date Issued
    Tuesday, March 10, 2015
    9 years ago
Abstract
A system with a nonstatistical translation component integrated with a statistical translation component engine. The same corpus may be used for training the statistical engine and also for determining when to use the statistical engine and when to use the translation component. This training may use probabilistic techniques. Both the statistical engine and the translation components may be capable of translating the same information, however the system determines which component to use based on the training. Retraining can be carried out to add additional components, or when after additional translator training.
Description
BACKGROUND

Statistical machine translation automatically learns how to translate using a training corpus. The learned information can then be used to translate another, “unknown” text, using information that the machine learned from the training operation.


However, current statistical machine translation models are typically not suited for certain types of expressions, e.g., those where statistical substitution is not possible or feasible. For example, the current state of statistical machine translation systems does not allow translating Chinese numbers into English until the numbers have been seen and the correct translation has been learned. Similar issues may exist for translations of names, dates, and other proper nouns.


In addition, it may be desirable to conform a machine translation output to certain formats. The most desirable format may be different than the training corpus, or inconsistent within the training corpus. As an example, Chinese names may be present in a training corpus with the family name first, followed by the surname. However, it is more conventional to print the translation in English with the first name first. This may make it desirable to change the output in order to deviate what was seen in the parallel training data.


Certain modern statistical machine translation systems have integrated a rule based translation component for things like numbers and dates. There have also been attempts to combine statistical translation with other full sentence machine translation systems by performing an independent translation with the different systems and deciding which of the systems provides a better translation.


SUMMARY

An aspect of the present system is to integrate non-statistical translation components, along with statistical components, to use certain components for certain kinds of translation. An aspect allows training to determine when it is desirable to use different components for different parts of the translation operation.


The techniques described herein use a parallel training corpus. The system may automatically learn from the corpuses where entity translation component or components are likely to produce or better translations. This system can automatically learn a confidence factor for different entity translation components in specific contexts. Therefore, this approach can also adapt to unreliable entity translation components.





BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects will now be described in detail with reference to the accompanying drawings, wherein:



FIG. 1 shows a block diagram of the translation system;



FIG. 2 shows a flowchart of training a classifier that determines when to use different components for different translations; and



FIG. 3 shows a flowchart of operation using multiple translation components.





DETAILED DESCRIPTION

The present system describes integration of non-statistical machine translation components into a statistical machine translation framework. This is done by using a processing device to determine automatically which parts of an input string should use a “baseline” machine translation system, and which parts should use another entity translation component or components, referred to herein as the translation component.



FIG. 1 illustrates an exemplary hardware device which may execute the operation that is described with reference to the flowcharts of FIGS. 2 and 3. For the application of language translation, a processing module 100 receives data from various sources 105. The sources may be parallel corpora of multiple language information. Specifically, the sources may include translation memories, dictionaries, glossaries, Internet information, parallel corpora in multiple languages, non-parallel corpora in multiple languages having similar subject matter, and human-created translations. The processor 100 processes this information to produce translation parameters which are output as 110. The translation parameters are used by language engine 120 in making translations based on input language 130. In the disclosed embodiment, the language engine 120 includes a statistical engine 123, and at least one translation component 124. The language engine translates from a first language to a second language. However, alternatively, the speech engine can be any engine that operates on strings of words, such as a language recognition device, a speech recognition device, a machine paraphraser, natural language generator, modeler, or the like.


The processor 100 and speech engine 120 may be any general purpose computer, and can be effected by a microprocessor, a digital signal processor, or any other processing device that is capable of executing the operations described herein.


The flowcharts described herein can be instructions which are embodied on a machine-readable medium such as a disc or the like. Alternatively, the flowchart can be executed by dedicated hardware, or by any known or later discovered processing device.


The translation component 124 can be any existing translation component of any type, including a rule-based translator, or any other kind of machine translation component. Such translation components may be capable of translating many different kinds of information from one language to another.


In the embodiment, translation component 124 is used to translate only a portion of the information that it is capable of translating. For example, the translation component may be capable of translating standard two or three character Chinese names. This may apply to many different Chinese size strings. This may include, for example, certain strings which are not actually names. One aspect of the system is to identify the portions with are desired to be translated by the translation component. For example, in the above example, the component must determine how to identify the Chinese names in text, and then to translate those names using the component 124. Other Chinese language information is translated using the statistical engine 123.


Another aspect detects whether the translation component uses a complete and/or accurate rule set. For example, if the rule set for the translation component 124 for a specific translation is incomplete, then the engine 120 will consider using instead the baseline statistical machine translation part 123.


Using the above example, therefore, the goal is to identify Chinese names where the translation component 124 produces a correct translation. The translation component can therefore be used for entities that are not actually person names and can be translated; for example, company names that are constructed like person names.


Therefore, the training of the machine trains not only the statistical machine translation, but also trains when to use the statistical machine translation. The translator is given a source sentence in a source language, for example Chinese, which is to be translated into a target language, for example English. Among all possible target sentences, the machine may choose the sentence with the highest probability










e
^

=



arg





max

e



{

Pr


(

e
|
f

)


}






(
1
)







Where the symbol Pr(.) represents general probability distributions with no, or virtually no, assumptions, argmax, denotes the search for the output sentence in the target language, and e is the sentence.


The posterior probability is modeled using a log linear model. This framework produces a set of M feature functions hm(e,f), m−1 . . . M.


Each feature function M also has a model parameter λm, where m=1 . . . M.


The direct translation probability is given by:













Pr


(

e
|
f

)


=


p

λ
j
M




(

e
|
f

)








=


exp


[




m
=
1

M




λ
m




h
m



(

e
,
f

)




]






e





exp


[




m
=
1

M




λ
m




h
m



(


e


,
f

)




]














(
2
)




















(
3
)


















information may be translated by developing feature functions that capture the relevant properties of the translation task. These basic feature functions may include the alignment template approach described in “Discriminative Training And Maximum Entropy Models For Statistical Machine Translation”, Och and Ney 2002, proceedings of the 40th annual meeting of the Association for computational linguistics. This translation model segments the input sentence into phrases, translates these phrases, and reorders the translations into the target language.


Another possible feature function is a trigram language model. The feature functions may be trained using the unsmoothed maximum BLEU criterion, described in minimum error rate training in statistical machine translation (Och, 2003).


Training procedures for obtaining alignment templates is described in (Och 1999). Computation of word alignment in the parallel training corpus may use an expectation maximization technique, and a statistical alignment technique. See for example (Och and Ney 2003). This word alignment forms the basis for computing the probabilistic phrase to phrase translation lexicon p(e|f), which is used to store the translation of the phrase.


The translation component 124 is a machine translation system or module that can translate specific source language expressions into corresponding target language expressions. The translation component may provide the translation that is “best”, or may alternatively combine a candidate list of translation possibilities.


Different environments may use different translations. For example, the translation components may include:

    • a Chinese name translation—this translation component is a simple rule-based translation component that operates for two and three character Chinese names. This is done by applying the Pinyin rules to Chinese characters that frequently occur as parts of names, to identify and translate those Chinese names.
    • Number translation—this translation component performs a rule-based translation of Chinese numbers, percentages, and time expressions. It operates by determining such numbers percentages and time expressions, and translating them using rules.
    • Date translation—this translation component translates the expressions. One example is Nov. 2, 1971. The translation component will automatically translate this to the proper language.


An important issue is integration of these components with the statistical translator and training of when to use which one.


An ideal translation component provides no wrong translations at all. It provides the set of all correct translations for a given substring. Real world translation components make errors, and provide incorrect translations. For example, the Chinese name entity translation component frequently generates wrong translations when applied to Korean names. Certain expressions cannot be easily translated by the component. For example the date translator may provide 27 days, or the 27th as potential translations of the same characters. Only one of the two is correct for a specific context. Proper integration of the statistical translator with a translation component, therefore, requires learning/training when to use each of the components, and also training of the proper format to output.



FIG. 2 shows a flowchart showing how to learn automatically from a set of translation components in a parallel corpus, and to determine automatically which of the statistical engine 123, or the translation component 124, should be used to translate the source language string.


At 200, a translation component is annotated to list each substring that is capable of being translated by a translation component. Note that there may be one or many different translation components. The annotated corpus indicates which words/portions in the corpus can be translated with any of those translation components. That is done by determining words in the source language, that have a translation, via a translation component, actually occurring in the corresponding target language segment.


In an implementation, this may be carried out by applying all the translation components to all the source language substrings of the training corpus. The target language corpus may be used to determine if the training components has produced a correct translation.


A variant filter at 210 is used to attempt to prevent different forms of the same word from being rejected. The translation component at 200 may classify a correct translation as being wrong if the parallel training corpus is used as a variant of what the training component has proposed. The variant filter may analyze all or many of the possible translations. For example, all of the following strings: a thousand, one thousand or 1000, refer to the same number. Any of these is the correct translation of the Chinese word for “thousand”. The variant filter may allow any of these translations to be accepted.


It may be desirable to provide enough precision in the translation component to avoid negative instances as being misclassified as positive instances.


At 220, the annotated corpus is used for classifier training. A probabilistic classifier is trained based on the data. The classifier may be part of the processor 100. The classifier determines, for each source language sub string, and its source language context, if the translation component has actually produced a correct translation, or not a correct translation.


In operation, given a large parallel training corpus, a very large annotated corpus may be automatically generated. For language pairs like Chinese/English and Arabic/English, there may be readily available parallel corpora of more than 100 million words. Human-annotated training corpora are typically much smaller, e.g., they may be rarely less than larger than one million words.


Another aspect is that the automated annotation may be directly oriented toward the ultimate goal which is to use a certain translation component to produce correct translations. As a result, those instances for which the translation component produces a wrong translation may be annotated as negative instances.


When the translation component 124 is improved via increased coverage or improved quality of translation, an annotated corpus can be automatically regenerated at 230. The model may then be retrained to detect when to use the improved training corpus. Similarly, re-training can occur when the statistical database 123 is improved, when a new translation component is added, or when some other situation occurs.


This allows integration of different training components that each translate the same kind of instructions. The system learns automatically in this way when to trust which translation component. This allows automatic determination of which are acceptable and not acceptable translation components for particular words in particular contexts.


Mathematically speaking, to determine if the certain source language substrate of a source language string can be translated with the correct translation component to produce the translation, a model can be trained according to:

p(c|fj1j2,fj1−2j1−1,fj2+1j2+2,TCn,e11)  (4)


Where fj represent substrings of a source language string; TCn is a specific translation component, and c stands for the two situations where “the translation component produces the correct translation” or “the translation component does not produce the correct translation”. A standard maximum entropy model described by Berger 96 may be used that uses each single dependent variable in equation 4 as a feature, is combined with the class c.


Different classifier models may be used for this framework, besides the maximum entropy classifier. A maximum entropy classifier may obtain probabilities which can be reasonably compared for different substrings.



FIG. 3 shows the overall operation of using the engine. The classifier is trained at 300, using the flowchart of FIG. 2. Once the classifier is trained in this way, the translation component is integrated into the overall process of the phrase based statistical machine translation system at 310. Each sub string of the text to be translated is analyzed at 320. The operation computes the probability that the translation component will produce a correct translation. A filter at 330 uses a threshold pmin to filter those cases where the probability of correct translation is too low. The resulting set of named entities is then used as an additional phrase translation candidates. These are hypothesized in search together with the phrases of the baseline statistical machine translation system at 340.


The statistical machine translation system balances between the use of translation component phrases and baseline system phrases. This may be defined by an additional feature function which counts the number of translation component phrases that are used. This may be stored as a variable referred to as TC-PENALTY. Other feature functions, such as a language model, or a reordering model, may also score those phrases.


Another aspect may enforce the use of translation component phrases if the corresponding source language sub string is rarely seen.


The translation component may also be integrated into the word alignment process between the parallel corpora. This may be done to improve word alignment accuracy during training. This procedure may automatically detect whether the translation component is trained sufficiently to be reliable. Once the translation components is sufficiently reliable, that information can be used to constrain the word alignment generated in the training procedure better alignment between the two languages may be obtained by using the translation components for certain phrases.


This training may use different statistical alignment models such as the IBM model 1, the HMM, and/or the IBM model 4. This constraint may also be integrated by constraining the set of considered alignments in the expectation maximization algorithm. This constraint may also improve the alignment quality of the surrounding words. For example, there may be a first order dependence among the alignment positions of the HMM and model for alignment models.


Some exemplary results are provided to explain the concepts. The results are based on a Chinese to English translation which was done in 2003. Table 1 provides statistics on the training, development and test corpus that was used. There are four reference translations, from the training corpus (train small, train large, dev and test.)









TABLE 1







Characteristics of training corpus (Train),


development corpus (Dev), test corpus (Test).










Chinese
English













Train
Segments

5 109


(small)
Words
89 121
111 251


Train
Segments

 6.9M


(large)
Words
170 M
157M


Dev
Segments

935



Words
27 012
27.6 K-30.1 K


Test
Segments

878



Words
24 540
25.3 K-28.6 K









The system uses a subset of 128,000 sentences from the large parallel corpus to generate the translation component works-annotated corpus. Based on this corpus, 264,488 Chinese substrings can be translated using any of the rule based translation component, suggesting altogether approximately 364,000 translations. 60,589 of those translations, or 16.6%, also occur in the corresponding target language; called positive instances.


A review of these annotations shows that positive instances of the automatic corpus annotation are rarely incorrectly annotated, on the other hand, negative instances are much more frequent due to the existence of sentence alignment errors, and insufficient recall of the translation component.


For evaluation purposes, the test corpus was annotated in the same way as the training database. The test corpus is perfectly sentence aligned, and therefore there are no wrong negative instances due to alignment. In the test corpus, there are 2529 substrings that the translation component can translate, and when it does, it suggests 3651 translations of which 1287 (35.3%) also occur in any of the four references.


Using that annotated training corpus, the maximum entropy classifier described above is trained. Table 2 provides the results of this classifier for the development Corp. this for various training corpus sizes. This experiment uses Pmin=0.2.









TABLE 2







Quality of classifier trained on the automatically annotated corpus













Strict

Loose


# Segments
Errors[%]
Precision [%]
Recall[%]
Precision[%]














1,000
18
79
65
88


2,000
17
85
63
90


4,000
16
86
67
91


8,000
14
88
70
92


16,000
13
89
71
94


32,000
11
92
75
95


64,000
9
94
78
97


128,000
8
95
80
97





(Errors[%]: error rate of classifier (percentage of suggested translations that are correct),


(Strict) Precision[%]/Recall[%]: precision and recall of classifier,


Loose Precision[%]: percentage of source language sub-strings where any of the suggested translations is correct).






In operation, a precision as high as 95% was eventually obtained with the recall of the person. See table 2 which shows the actual values. The column entitled “loose precision” provides a percentage of source language substrings where any of the suggested translations also occur in the references. Eventually the precision of 97% was achieved. This means that about 3% of the Chinese substrings for which a translation were not correct.


Word alignment that is computed by the statistical alignment models may be used to train the phrase based translation models, on those parts of the text where the automatic corpus annotation detects a translation. The automatic corpus annotation may be a very high precision, and can be used to improve the translation. One aspect, therefore, may improve general word alignment quality using the information in the translation component induced word alignment, in the statistical word alignment training.


Although only a few embodiments have been disclosed in detail above, other modifications are possible, and this disclosure is intended to cover all such modifications, and most particularly, any modification which might be predictable to a person having ordinary skill in the art. For example, the above has described integration of rule based translation components. It should be noted that other components, such as statistical components and the like may select alternative translations that can be used. The probability assigned by the model can be an additional feature for the classifier.


Also, only those claims which use the words “means for” are intended to be interpreted under 35 USC 112, sixth paragraph. Moreover, no limitations from the specification are intended to be read into any claims, unless those limitations are expressly included in the claims.

Claims
  • 1. A method comprising: executing instructions stored in memory via a processor of a language engine for: training the language engine to train for statistical machine translation;training the language engine when to use statistical machine translation by applying a machine learning method to a bilingual text that has been annotated with the output of a non-statistical translation component along with information identifying the type of the translation component;translating information from a first language to a second language using at least two translation components, wherein at least one translation component is a non-statistical translation component, each of the at least two translation components capable of translating equivalent phrases, each of the at least two translation components being selected based upon evaluation of an annotated training corpus, the annotated training corpus comprising substrings in the first language that have been annotated to associate the substrings with one or more translation components that are to be utilized to translate the substrings; andautomatically selecting a preferred component from the at least two translation components, the preferred component providing a translation having a highest probability of being correct.
  • 2. A method as in claim 1, further comprising defining a feature function that indicates when to use the at least two translation components.
  • 3. A method as in claim 1, wherein automatically selecting the preferred component comprises: obtaining a phrase to be translated; andcomputing a probability of being correct in translating the phrase for each of the at least two translation components.
  • 4. A method as in claim 3, further comprising: detecting variants of a translation of the phrase; andaccepting the translation of the phrase as being proper if the translation of the phrase is equivalent to one of the variants.
  • 5. A method as in claim 4, further comprising annotating a training corpus based on the variants to form an annotated training corpus.
  • 6. A method as in claim 1, wherein a first component of the at least two translation components comprises a statistical translator and a second component of the at least two translation components comprises a nonstatistical translator for proper nouns.
  • 7. A method as in claim 1, wherein a first component of the at least two translation components comprises a statistical translator and a second component of the at least two translation components comprises a nonstatistical translator for names.
  • 8. A method as in claim 1, wherein a first component of the at least two translation components comprises a statistical translator and a second component of the at least two translation components comprises a nonstatistical translator for numbers.
  • 9. A method as in claim 1, further comprising automatically re-selecting a second preferred component from the at least two translation components in response to an occurrence.
  • 10. A method as in claim 9, wherein the occurrence comprises adding an additional nonstatistical translation component to the at least two translation components.
  • 11. A method as in claim 9, wherein the occurrence comprises improving at least one of the at least two translation components.
  • 12. A method as in claim 1, further comprising: training a format of an output of a machine translation system based on a training corpus; andallowing at least one of a plurality of different formats to be selected from within the training corpus.
  • 13. A system comprising: a memory for storing executable instructions;a processor for executing the instructions for training a language engine to train for statistical machine translation and training the language engine when to use statistical machine translation by applying a machine learning method to a bilingual text that has been annotated with the output of a non-statistical translation component along with information identifying the type of the translation component;at least two translating parts stored in memory and executable by the processor, wherein at least one translating part is a non-statistical translation part, each of the at least two translating parts operational to translate information from a first language to a second language and each of the at least two translating parts capable of translating equivalent phrases, each of the at least two translating parts being selected based upon evaluation of an annotated training corpus, the annotated training corpus comprising substrings in the first language that have been annotated to associate the substrings with one or more translating parts that are to be utilized to translate the substrings; anda classifier part stored in memory and executable by the processor to automatically select a preferred component from the at least two translating parts, the preferred component providing a translation having a highest probability of being correct.
  • 14. A system as in claim 13, further comprising a training corpus.
  • 15. A system as in claim 14, further comprising an output module that formats an output based on the training corpus.
  • 16. A system as in claim 13, further comprising a variant detector that detects variants of a translated phrase and accepts the translated phrase as being proper if the translation phrase is one of the variants.
  • 17. A system as in claim 16, further comprising an annotated training corpus based, at least in part, on the variants.
  • 18. A system as in claim 13, wherein a first part of the at least two translating parts comprises a statistical translator component and a second part of the at least two translating parts comprises a nonstatistical translator component for proper nouns.
  • 19. A system as in claim 13, wherein a first part of the at least two translating parts comprises a statistical translator component and a second part of the at least two translating parts comprises a nonstatistical translator component for names.
  • 20. A system as in claim 13, wherein a first part of the at least two translating parts comprises a statistical translator component and a second part of the at least two translating parts comprises a nonstatistical translator component for numbers.
  • 21. A system as in claim 13, further comprising a feature part that indicates when to use the at least two translation components.
  • 22. A system as in claim 13, wherein the classifier part comprises a probabilistic classifier.
  • 23. A system as in claim 13, further comprising at least one additional translating part.
  • 24. A non-transitory computer readable storage medium having embodied thereon a program, the program executable by a processor to perform a method, the method comprising: training a language engine to train for statistical machine translation; training the language engine when to use statistical machine translation by applying a machine learning method to a bilingual text that has been annotated with the output of a non-statistical translation component along with information identifying the type of the translation component;translating information from a first language to a second language using at least two translation components, wherein at least one translation component is a non-statistical translation component, each of the at least two translation components capable of translating equivalent phrases, each of the at least two translation components being selected based upon evaluation of an annotated training corpus, the annotated training corpus comprising substrings in the first language that have been annotated to associate the substrings with one or more translation components that are to be utilized to translate the substrings; andautomatically selecting a preferred component from the at least two translation components, the preferred component providing a translation having a highest probability of being correct.
  • 25. The method as in claim 1, further comprising utilizing a statistical machine translator if the at least two translation components are using a rule set that is inaccurate.
  • 26. The method as in claim 1, further comprising generating an annotated training corpus by: applying a plurality of translation components to substrings in a first language to translate the substrings into a second language; andannotating strings in a first language for each translation component that accurately translates the substring into a second language.
  • 27. The method as in claim 26, further comprising evaluating context of a translation of a substring by evaluating the translated substring relative to a string from which the substring was obtained.
  • 28. The method as in claim 27, further comprising determining an accurate translation by ensuring that the translation is both correctly translated and contextually correct.
  • 29. The method as in claim 26, further comprising automatically regenerating the annotated training corpus when a quality of translations generated by at least one of the two translation components has increased.
  • 30. The method as in claim 1, selecting the at least two translation components by applying a maximum entropy model to each substring that is to be translated by the at least two translation components.
CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional and claims the priority of U.S. patent application Ser. No. 11/107,304, entitled “Selection and Use of Nonstatistical Translation Components in a Statistical Machine Translation Framework,” filed on Apr. 15, 2005, now U.S. Pat. No. 8,666,725, which claims the benefit of U.S. Provisional Patent Application No. 60/562,774, filed on Apr. 16, 2004, the subject matter of which are hereby incorporated by reference.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The U.S. Government may have certain rights in this invention pursuant to Grant No. N66001-00-1-8914 awarded by DARPA.

US Referenced Citations (360)
Number Name Date Kind
4502128 Okajima et al. Feb 1985 A
4599691 Sakaki et al. Jul 1986 A
4615002 Innes Sep 1986 A
4661924 Okamoto et al. Apr 1987 A
4787038 Doi et al. Nov 1988 A
4791587 Doi Dec 1988 A
4800522 Miyao et al. Jan 1989 A
4814987 Miyao et al. Mar 1989 A
4942526 Okajima et al. Jul 1990 A
4980829 Okajima et al. Dec 1990 A
5020112 Chou May 1991 A
5088038 Tanaka et al. Feb 1992 A
5091876 Kumano et al. Feb 1992 A
5146405 Church Sep 1992 A
5167504 Mann Dec 1992 A
5181163 Nakajima et al. Jan 1993 A
5212730 Wheatley et al. May 1993 A
5218537 Hemphill et al. Jun 1993 A
5220503 Suzuki et al. Jun 1993 A
5267156 Nomiyama Nov 1993 A
5268839 Kaji Dec 1993 A
5295068 Nishino et al. Mar 1994 A
5302132 Corder Apr 1994 A
5311429 Tominaga May 1994 A
5387104 Corder Feb 1995 A
5408410 Kaji Apr 1995 A
5432948 Davis et al. Jul 1995 A
5442546 Kaji et al. Aug 1995 A
5477450 Takeda et al. Dec 1995 A
5477451 Brown et al. Dec 1995 A
5495413 Kutsumi et al. Feb 1996 A
5497319 Chong et al. Mar 1996 A
5510981 Berger et al. Apr 1996 A
5528491 Kuno et al. Jun 1996 A
5535120 Chong et al. Jul 1996 A
5541836 Church et al. Jul 1996 A
5541837 Fushimoto Jul 1996 A
5548508 Nagami Aug 1996 A
5644774 Fukumochi et al. Jul 1997 A
5675815 Yamauchi et al. Oct 1997 A
5687383 Nakayama et al. Nov 1997 A
5696980 Brew Dec 1997 A
5724593 Hargrave, III et al. Mar 1998 A
5752052 Richardson et al. May 1998 A
5754972 Baker et al. May 1998 A
5761631 Nasukawa Jun 1998 A
5761689 Rayson et al. Jun 1998 A
5768603 Brown et al. Jun 1998 A
5779486 Ho et al. Jul 1998 A
5781884 Pereira et al. Jul 1998 A
5794178 Caid et al. Aug 1998 A
5805832 Brown et al. Sep 1998 A
5806032 Sproat Sep 1998 A
5819265 Ravin et al. Oct 1998 A
5826219 Kutsumi Oct 1998 A
5826220 Takeda et al. Oct 1998 A
5845143 Yamauchi et al. Dec 1998 A
5848385 Poznanski et al. Dec 1998 A
5848386 Motoyama Dec 1998 A
5855015 Shoham Dec 1998 A
5864788 Kutsumi Jan 1999 A
5867811 O'Donoghue Feb 1999 A
5870706 Alshawi Feb 1999 A
5893134 O'Donoghue et al. Apr 1999 A
5903858 Saraki May 1999 A
5907821 Kaji et al. May 1999 A
5909681 Passera et al. Jun 1999 A
5930746 Ting Jul 1999 A
5966685 Flanagan et al. Oct 1999 A
5966686 Heidorn et al. Oct 1999 A
5983169 Kozma Nov 1999 A
5987402 Murata et al. Nov 1999 A
5987404 Della Pietra et al. Nov 1999 A
5991710 Papineni et al. Nov 1999 A
5995922 Penteroudakis et al. Nov 1999 A
6018617 Sweitzer et al. Jan 2000 A
6031984 Walser Feb 2000 A
6032111 Mohri Feb 2000 A
6047252 Kumano et al. Apr 2000 A
6064819 Franssen et al. May 2000 A
6064951 Park et al. May 2000 A
6073143 Nishikawa et al. Jun 2000 A
6077085 Parry et al. Jun 2000 A
6092034 McCarley et al. Jul 2000 A
6119077 Shinozaki Sep 2000 A
6131082 Hargrave, III et al. Oct 2000 A
6161082 Goldberg et al. Dec 2000 A
6182014 Kenyon et al. Jan 2001 B1
6182027 Nasukawa et al. Jan 2001 B1
6205456 Nakao Mar 2001 B1
6206700 Brown et al. Mar 2001 B1
6223150 Duan et al. Apr 2001 B1
6233544 Alshawi May 2001 B1
6233545 Datig May 2001 B1
6233546 Datig May 2001 B1
6236958 Lange et al. May 2001 B1
6269351 Black Jul 2001 B1
6275789 Moser et al. Aug 2001 B1
6278967 Akers et al. Aug 2001 B1
6278969 King et al. Aug 2001 B1
6285978 Bernth et al. Sep 2001 B1
6289302 Kuo Sep 2001 B1
6304841 Berger et al. Oct 2001 B1
6311152 Bai et al. Oct 2001 B1
6317708 Witbrock et al. Nov 2001 B1
6327568 Joost Dec 2001 B1
6330529 Ito Dec 2001 B1
6330530 Horiguchi et al. Dec 2001 B1
6356864 Foltz et al. Mar 2002 B1
6360196 Poznanski et al. Mar 2002 B1
6389387 Poznanski et al. May 2002 B1
6393388 Franz et al. May 2002 B1
6393389 Chanod et al. May 2002 B1
6415250 van den Akker Jul 2002 B1
6460015 Hetherington et al. Oct 2002 B1
6470306 Pringle et al. Oct 2002 B1
6473729 Gastaldo et al. Oct 2002 B1
6480698 Ho et al. Nov 2002 B2
6490549 Ulicny et al. Dec 2002 B1
6498921 Ho et al. Dec 2002 B1
6502064 Miyahira et al. Dec 2002 B1
6529865 Duan et al. Mar 2003 B1
6535842 Roche et al. Mar 2003 B1
6587844 Mohri Jul 2003 B1
6609087 Miller et al. Aug 2003 B1
6647364 Yumura et al. Nov 2003 B1
6691279 Yoden et al. Feb 2004 B2
6745161 Arnold et al. Jun 2004 B1
6745176 Probert, Jr. et al. Jun 2004 B2
6757646 Marchisio Jun 2004 B2
6778949 Duan et al. Aug 2004 B2
6782356 Lopke Aug 2004 B1
6810374 Kang Oct 2004 B2
6848080 Lee et al. Jan 2005 B1
6857022 Scanlan Feb 2005 B1
6885985 Hull Apr 2005 B2
6901361 Portilla May 2005 B1
6904402 Wang et al. Jun 2005 B1
6952665 Shimomura et al. Oct 2005 B1
6983239 Epstein Jan 2006 B1
6996518 Jones et al. Feb 2006 B2
6996520 Levin Feb 2006 B2
6999925 Fischer et al. Feb 2006 B2
7013262 Tokuda et al. Mar 2006 B2
7016827 Ramaswamy et al. Mar 2006 B1
7016977 Dunsmoir et al. Mar 2006 B1
7024351 Wang Apr 2006 B2
7031911 Zhou et al. Apr 2006 B2
7050964 Menzes et al. May 2006 B2
7085708 Manson Aug 2006 B2
7089493 Hatori et al. Aug 2006 B2
7103531 Moore Sep 2006 B2
7107204 Liu et al. Sep 2006 B1
7107215 Ghali Sep 2006 B2
7113903 Riccardi et al. Sep 2006 B1
7143036 Weise Nov 2006 B2
7146358 Gravano et al. Dec 2006 B1
7149688 Schalkwyk Dec 2006 B2
7171348 Scanlan Jan 2007 B2
7174289 Sukehiro Feb 2007 B2
7177792 Knight et al. Feb 2007 B2
7191115 Moore Mar 2007 B2
7194403 Okura et al. Mar 2007 B2
7197451 Carter et al. Mar 2007 B1
7206736 Moore Apr 2007 B2
7209875 Quirk et al. Apr 2007 B2
7219051 Moore May 2007 B2
7239998 Xun Jul 2007 B2
7249012 Moore Jul 2007 B2
7249013 Al-Onaizan et al. Jul 2007 B2
7283950 Pournasseh et al. Oct 2007 B2
7295962 Marcu Nov 2007 B2
7302392 Thenthiruperai et al. Nov 2007 B1
7319949 Pinkham Jan 2008 B2
7340388 Soricut et al. Mar 2008 B2
7346487 Li Mar 2008 B2
7346493 Ringger et al. Mar 2008 B2
7349839 Moore Mar 2008 B2
7349845 Coffman et al. Mar 2008 B2
7356457 Pinkham et al. Apr 2008 B2
7369998 Sarich et al. May 2008 B2
7373291 Garst May 2008 B2
7383542 Richardson et al. Jun 2008 B2
7389222 Langmead et al. Jun 2008 B1
7389234 Schmid et al. Jun 2008 B2
7403890 Roushar Jul 2008 B2
7409332 Moore Aug 2008 B2
7409333 Wilkinson et al. Aug 2008 B2
7447623 Appleby Nov 2008 B2
7454326 Marcu et al. Nov 2008 B2
7496497 Liu Feb 2009 B2
7533013 Marcu May 2009 B2
7536295 Cancedda et al. May 2009 B2
7546235 Brockett et al. Jun 2009 B2
7552053 Gao et al. Jun 2009 B2
7565281 Appleby Jul 2009 B2
7574347 Wang Aug 2009 B2
7580830 Al-Onaizan et al. Aug 2009 B2
7587307 Cancedda et al. Sep 2009 B2
7620538 Marcu et al. Nov 2009 B2
7620632 Andrews Nov 2009 B2
7624005 Koehn et al. Nov 2009 B2
7624020 Yamada et al. Nov 2009 B2
7627479 Travieso et al. Dec 2009 B2
7680646 Lux-Pogodalla et al. Mar 2010 B2
7689405 Marcu Mar 2010 B2
7698124 Menezes et al. Apr 2010 B2
7698125 Graehl et al. Apr 2010 B2
7707025 Whitelock Apr 2010 B2
7711545 Koehn May 2010 B2
7716037 Precoda et al. May 2010 B2
7813918 Muslea et al. Oct 2010 B2
7822596 Elgazzar et al. Oct 2010 B2
7925494 Cheng et al. Apr 2011 B2
7957953 Moore Jun 2011 B2
7974833 Soricut et al. Jul 2011 B2
8060360 He Nov 2011 B2
8145472 Shore et al. Mar 2012 B2
8214196 Yamada et al. Jul 2012 B2
8244519 Bicici et al. Aug 2012 B2
8265923 Chatterjee et al. Sep 2012 B2
8326598 Macherey et al. Dec 2012 B1
8886515 Van Assche Nov 2014 B2
8886517 Soricut et al. Nov 2014 B2
8886518 Wang et al. Nov 2014 B1
20010009009 Iizuka Jul 2001 A1
20010029455 Chin et al. Oct 2001 A1
20020002451 Sukehiro Jan 2002 A1
20020013693 Fuji Jan 2002 A1
20020040292 Marcu Apr 2002 A1
20020046018 Marcu et al. Apr 2002 A1
20020046262 Heilig et al. Apr 2002 A1
20020059566 Delcambre et al. May 2002 A1
20020078091 Vu et al. Jun 2002 A1
20020087313 Lee et al. Jul 2002 A1
20020099744 Coden et al. Jul 2002 A1
20020111788 Kimpara Aug 2002 A1
20020111789 Hull Aug 2002 A1
20020111967 Nagase Aug 2002 A1
20020143537 Ozawa et al. Oct 2002 A1
20020152063 Tokieda et al. Oct 2002 A1
20020169592 Aityan Nov 2002 A1
20020188438 Knight et al. Dec 2002 A1
20020188439 Marcu Dec 2002 A1
20020198699 Greene et al. Dec 2002 A1
20020198701 Moore Dec 2002 A1
20020198713 Franz et al. Dec 2002 A1
20030009322 Marcu Jan 2003 A1
20030023423 Yamada et al. Jan 2003 A1
20030144832 Harris Jul 2003 A1
20030154071 Shreve Aug 2003 A1
20030158723 Masuichi et al. Aug 2003 A1
20030176995 Sukehiro Sep 2003 A1
20030182102 Corston-Oliver et al. Sep 2003 A1
20030191626 Al-Onaizan et al. Oct 2003 A1
20030204400 Marcu et al. Oct 2003 A1
20030216905 Chelba et al. Nov 2003 A1
20030217052 Rubenczyk et al. Nov 2003 A1
20030233222 Soricut et al. Dec 2003 A1
20040015342 Garst Jan 2004 A1
20040024581 Koehn et al. Feb 2004 A1
20040030551 Marcu et al. Feb 2004 A1
20040035055 Zhu et al. Feb 2004 A1
20040044530 Moore Mar 2004 A1
20040059708 Dean et al. Mar 2004 A1
20040068411 Scanlan Apr 2004 A1
20040098247 Moore May 2004 A1
20040102956 Levin May 2004 A1
20040102957 Levin May 2004 A1
20040111253 Luo et al. Jun 2004 A1
20040115597 Butt Jun 2004 A1
20040122656 Abir Jun 2004 A1
20040167768 Travieso et al. Aug 2004 A1
20040167784 Travieso et al. Aug 2004 A1
20040193401 Ringger et al. Sep 2004 A1
20040230418 Kitamura Nov 2004 A1
20040237044 Travieso et al. Nov 2004 A1
20040260532 Richardson et al. Dec 2004 A1
20050021322 Richardson et al. Jan 2005 A1
20050021517 Marchisio Jan 2005 A1
20050026131 Elzinga et al. Feb 2005 A1
20050033565 Koehn Feb 2005 A1
20050038643 Koehn Feb 2005 A1
20050055199 Ryzchachkin et al. Mar 2005 A1
20050055217 Sumita et al. Mar 2005 A1
20050060160 Roh et al. Mar 2005 A1
20050075858 Pournasseh et al. Apr 2005 A1
20050086226 Krachman Apr 2005 A1
20050102130 Quirk et al. May 2005 A1
20050125218 Rajput et al. Jun 2005 A1
20050149315 Flanagan et al. Jul 2005 A1
20050171757 Appleby Aug 2005 A1
20050204002 Friend Sep 2005 A1
20050228640 Aue et al. Oct 2005 A1
20050228642 Mau et al. Oct 2005 A1
20050228643 Munteanu et al. Oct 2005 A1
20050234701 Graehl et al. Oct 2005 A1
20050267738 Wilkinson et al. Dec 2005 A1
20060004563 Campbell et al. Jan 2006 A1
20060015320 Och Jan 2006 A1
20060015323 Udupa et al. Jan 2006 A1
20060018541 Chelba et al. Jan 2006 A1
20060020448 Chelba et al. Jan 2006 A1
20060041428 Fritsch et al. Feb 2006 A1
20060095248 Menezes et al. May 2006 A1
20060111891 Menezes et al. May 2006 A1
20060111892 Menezes et al. May 2006 A1
20060111896 Menezes et al. May 2006 A1
20060129424 Chan Jun 2006 A1
20060142995 Knight et al. Jun 2006 A1
20060150069 Chang Jul 2006 A1
20060167984 Fellenstein et al. Jul 2006 A1
20060190241 Goutte et al. Aug 2006 A1
20070016400 Soricutt et al. Jan 2007 A1
20070016401 Ehsani et al. Jan 2007 A1
20070033001 Muslea et al. Feb 2007 A1
20070043553 Dolan Feb 2007 A1
20070050182 Sneddon et al. Mar 2007 A1
20070078654 Moore Apr 2007 A1
20070078845 Scott et al. Apr 2007 A1
20070083357 Moore et al. Apr 2007 A1
20070094169 Yamada et al. Apr 2007 A1
20070112553 Jacobson May 2007 A1
20070112555 Lavi et al. May 2007 A1
20070112556 Lavi et al. May 2007 A1
20070122792 Galley et al. May 2007 A1
20070168202 Changela et al. Jul 2007 A1
20070168450 Prajapat et al. Jul 2007 A1
20070180373 Bauman et al. Aug 2007 A1
20070219774 Quirk et al. Sep 2007 A1
20070250306 Marcu et al. Oct 2007 A1
20070265825 Cancedda et al. Nov 2007 A1
20070265826 Chen et al. Nov 2007 A1
20070269775 Andreev et al. Nov 2007 A1
20070294076 Shore et al. Dec 2007 A1
20080052061 Kim et al. Feb 2008 A1
20080065478 Kohlmeier et al. Mar 2008 A1
20080114583 Al-Onaizan et al. May 2008 A1
20080154581 Lavi et al. Jun 2008 A1
20080183555 Walk Jul 2008 A1
20080215418 Kolve et al. Sep 2008 A1
20080249760 Marcu et al. Oct 2008 A1
20080270112 Shimohata Oct 2008 A1
20080281578 Kumaran et al. Nov 2008 A1
20080307481 Panje Dec 2008 A1
20090076792 Lawson-Tancred Mar 2009 A1
20090083023 Foster et al. Mar 2009 A1
20090119091 Sarig May 2009 A1
20090125497 Jiang et al. May 2009 A1
20090234634 Chen et al. Sep 2009 A1
20090241115 Raffo et al. Sep 2009 A1
20090326912 Ueffing Dec 2009 A1
20100017293 Lung et al. Jan 2010 A1
20100042398 Marcu et al. Feb 2010 A1
20100138213 Bicici et al. Jun 2010 A1
20100174524 Koehn Jul 2010 A1
20110029300 Marcu et al. Feb 2011 A1
20110066643 Cooper et al. Mar 2011 A1
20110082684 Soricut et al. Apr 2011 A1
20120323554 Hopkins et al. Dec 2012 A1
Foreign Referenced Citations (8)
Number Date Country
0469884 Feb 1992 EP
0715265 Jun 1996 EP
0933712 Aug 1999 EP
0933712 Jan 2001 EP
07244666 Sep 1995 JP
10011447 Jan 1998 JP
11272672 Oct 1999 JP
WO03083709 Oct 2003 WO
Non-Patent Literature Citations (256)
Entry
Robert Frederking and Sergei Nirenburg. 1994. Three heads are better than one. In Proceedings of the 4th Conference on Applied Natural Language Processing, pp. 95-100, Stuttgart, Germany.
Och, Franz Josef and Hermann Ney. 2002. Discriminative training and maximum entropy models for statistical machine translation. In Proc. of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA.
Yasuda, K., Sugaya, F., Takezawa, T., Yamamoto, S. and Yanagida, M. 2002 Automatic Machine Translation Selection Scheme to Output the Best Result, Proc. of LREC.
Koehn, Philipp, “Noun Phrase Translation,” a Ph.D. dissertation for the University of Southern California, pp. xiii, 23, 25-47, 72-81, Dec. 2003.
Liu, Qun, “A Chinese-English Machine Translation System based on Micro-Engine Architecture,” an International Conference on Translation and Information Technology, Hong Kong, Dec. 2000, pp. 1-10.
Ren, Fuji and Shi, Hongchi, “Parallel Machine Translation: Principles and Practice,” Proceedings of Seventh IEEE International Conference on Engineering of Complex Computer Systems, 2001, pp. 249-259.
Uchimoto, K. et al., “Word Translation by Combining Example-Based Methods and Machine-Learning Models,” Natural Language Processing (Shizen Gengo Shori), vol. 10, No. 3., Apr. 2003, pp. 87-114.
Uchimoto, K. et al., “Word Translation by Combining Example-Based Methods and Machine-Learning Models,” Natural Language Processing (Shizen Gengo Shori), vol. 10, No. 3., Apr. 2003, pp. 87-114, (English translation).
Zhang et al., “Distributed Language Modeling for N-best List Re-ranking,” In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (Sydney, Australia, Jul. 22-23, 2006). ACL Workshops. Assoc. for Computational Linguistics, Morristown, NJ, 216-223.
“Patent Cooperation Treaty International Preliminary Report on Patentability and The Written Opinion, International application No. PCT/US2008/004296, Oct. 6, 2009, 5 pgs.”
Document, Wikipedia.com, web.archive.org (Feb. 24, 2004) <http://web.archive.org/web/20040222202831 /http://en.wikipedia.org/wikiiDocument>.
Identifying, Dictionary.com, wayback.archive.org (Feb. 28, 2007) <http://wayback.archive.org/web/200501 01OOOOOO*/http:////dictionary.reference.com//browse//identifying>, <http://web.archive.org/web/20070228150533/http://dictionary.reference.com/browse/identifying>.
Elhadad, M. and Robin, J., “Controlling Content Realization with Functional Unification Grammars”, 1992, Aspects of Automated Natural Language Generation, Dale et al. (eds)., Springer Verlag, pp. 89-104.
“Elhadad, Michael, ““FUF: the Universal Unifier User Manual Version 5.2””, 1993, Department of Computer Science,Ben Gurion University, Beer Sheva, Israel.”
“Elhadad, Michael, ““Using Argumentation to Control Lexical Choice: A Functional Unification Implementation””,1992, Ph.D. Thesis, Graduate School of Arts and Sciences, Columbia University.”
“Elhadad. M., and Robin, J., ““SURGE: a Comprehensive Plug-in Syntactic Realization Component for Text Generation””, 1999 (available at http://www.cs.bgu.ac.il/-elhadad/pub.html).”
Fleming, Michael et al., “Mixed-Initiative Translation of Web Pages,” AMTA 2000, LNAI 1934, Springer-Verlag, Berlin, Germany, 2000, pp. 25-29.
Franz Josef Och, Hermann Ney: “Improved Statistical Alignment Models” ACLOO:Proc. of the 38th Annual Meeting of the Association for Computational Lingustics, ′Online! Oct. 2-6, 2000, pp. 440-447, XP002279144 Hong Kong, China Retrieved from the Internet: <URL:http://www-i6.informatik.rwth-aachen.de/Colleagues/och/ACLOO.ps> retrieved on May 6, 2004! abstract.
Fung et al, “Mining Very-non parallel corpora: Parallel sentence and lexicon extractioin via bootstrapping and EM”, In EMNLP 2004.
“Fung, P. and Yee, L., ““An IR Approach for Translating New Words from Nonparallel, Comparable Texts””, 1998,36th Annual Meeting of the ACL, 17th International Conference on Computational Linguistics, pp. 414-420.”
“Fung, Pascale, ““Compiling Bilingual Lexicon Entries From a Non-Parallel English-Chinese Corpus””, 1995, Proc, of the Third Workshop on Very Large Corpora, Boston, MA, pp. 173-183.”
“Gale, W. and Church, K., ““A Program for Aligning Sentences in Bilingual Corpora,”” 1991, 29th Annual Meeting of the ACL, pp. 177-183.”
Gale, W. and Church, K., “A Program for Aligning Sentences in Bilingual Corpora,” 1993, Computational . Linguisitcs, vol. 19, No. 1, pp. 177-184.
Galley et al., “Scalable Inference and Training of Context-Rich Syntactic Translation Models,” Jul. 2006, in Proc. of the 21st International Conference on Computational Linguistics, pp. 961-968.
Galley et al., “What's in a translation rule?”, 2004, in Proc. of HLT/NAACL '04, pp. 1-8.
Gaussier et al, “A Geometric View on Bilingual Lexicon Extraction from Comparable Corpora”, In Proceedings of ACL Jul. 2004.
“Germann et al., ““Fast Decoding and Optimal Decoding for Machine Translation””, 2001, Proc. of the 39th Annual Meeting of the ACL, Toulouse, France, pp. 228-235.”
“Germann, Ulrich: ““Building a Statistical Machine Translation System from Scratch: How Much Bang for theBuck Can We Expect?”” Proc. of the Data-Driven MT Workshop of ACL-01, Toulouse, France, 2001.”
Gildea, D., “Loosely Tree-based Alignment for Machine Translation,” In Proceedings of the 41st Annual Meeting on Assoc. for Computational Linguistics—vol. 1 (Sapporo, Japan, Jul. 7-12, 2003). Annual Meeting of the ACL Assoc. for Computational Linguistics, Morristown, NJ, 80-87. DOI=http://dx.doi.org/10.3115/1075096.1075107.
“Grefenstette, Gregory, ““The World Wide Web as a Resource for Example-Based Machine Translation Tasks””, 1999, Translating and the Computer 21, Proc. of the 21 st International Cant. on Translating and the Computer. London, UK, 12 pp.”
Grossi et al, “Suffix Trees and their applications in string algorithms”, In. Proceedings of the 1st South American Workshop on String Processing, Sep. 1993, pp. 57-76.
Gupta et al., “Kelips: Building an Efficient and Stable P2P DHT thorough Increased Memory and Background Overhead,” 2003 IPTPS, LNCS 2735, pp. 160-169.
Habash, Nizar, “The Use of a Structural N-gram Language Model in Generation-Heavy Hybrid Machine Translation,” University of Maryland, Univ. Institute for Advance Computer Studies, Sep. 8, 2004.
“Hatzivassiloglou, V. et al., ““Unification-Based Glossing””,. 1995, Proc. of the International Joint Conference on Artificial Intelligence, pp. 1382-1389.”
Huang et al., “Relabeling Syntax Trees to Improve Syntax-Based Machine Translation Quality,” Jun. 4-9, 2006, in Proc. of the Human Language Techology Conference of the North Americna Chapter of the ACL, pp. 240-247.
Ide, N. and Veronis, J., “Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art”, Mar. 1998, Computational Linguistics, vol. 24, Issue 1, pp. 2-40.
Ikel, D., Schwartz, R., and Weischedei, R., “An Algorithm that learns What's in a Name,” Machine Learning 34, 211-231 (1999).
Imamura et al., “Feedback Cleaning of Machine Translation Rules Using Automatic Evaluation,” 2003 Computational Linguistics, pp. 447-454.
Imamura, Kenji, “Hierarchical Phrase Alignment Harmonized with Parsing”, 2001, in Proc. of NLPRS, Tokyo.
“Jelinek, F., ““Fast Sequential Decoding Algorithm Using a Stack””, Nov. 1969, IBM J. Res. Develop., vol. 13, No. 6, pp. 675-685.”
“Jones, K. Sparck, ““Experiments in Relevance Weighting of Search Terms””, 1979, Information Processing &Management, vol. 15, Pergamon Press Ltd., UK, pp. 133-144.”
Klein et al., “Accurate Unlexicalized Parsing,” Jul. 2003, in Proc. of the 41st Annual Meeting of the ACL, pp. 423-430.
“Knight et al., ““Integrating Knowledge Bases and Statistics in MT,”” 1994, Proc. of the Conference of the Association for Machine Translation in the Americas.”
“Knight et al., ““Filling Knowledge Gaps in a Broad-Coverage Machine Translation System””, 1995, Proc. ofthe14th International Joint Conference on Artificial Intelligence, Montreal, Canada, vol. 2, pp. 1390-1396.”
“Knight, K. and Al-Onaizan, Y., ““A Primer on Finite-State Software for Natural Language Processing””, 1999 (available at http://www.isLedullicensed-sw/carmel).”
Knight, K. and Al-Onaizan, Y., “Translation with Finite-State Devices,” Proceedings of the 4th AMTA Conference, 1998.
“Knight, K. and Chander, I., ““Automated Postediting of Documents,”” 1994, Proc. of the 12th Conference on Artificial Intelligence, pp. 779-784.”
Knight, K. and Graehl, J., “Machine Transliteration”, 1997, Proc. of the ACL-97, Madrid, Spain, pp. 128-135.
“Knight, K. and Hatzivassiloglou, V., ““Two-Level, Many-Paths Generation,”” D 1995, Proc. of the 33rd Annual Conference of the ACL, pp. 252-260.”
“Knight, K. and Luk, S., ““Building a Large-Scale Knowledge Base for Machine Translation,”” 1994, Proc. of the 12th Conference on Artificial Intelligence, pp. 773-778.”
“Knight, K. and Marcu, D., ““Statistics-Based Summarization—Step One: Sentence Compression,”” 2000, American Association for Artificial Intelligence Conference, pp. 703-710.”
“Knight, K. and Yamada, K., ““A Computational Approach to Deciphering Unknown Scripts,”” 1999, Proc. of the ACL Workshop on Unsupervised Learning in Natural Language Processing.”
“Knight, Kevin, ““A Statistical MT Tutorial Workbook,”” 1999, JHU Summer Workshop (available at http://www.isLedu/natural-language/mUwkbk.rtf).”
Knight, Kevin, “Automating Knowledge Acquisition for Machine Translation,” 1997, AI Magazine 18(4).
Knight, Kevin, “Connectionist Ideas and Algorithms,” Nov. 1990, Communications of the ACM, vol. 33, No. 11, pp. 59-74.
Knight, Kevin, “Decoding Complexity in Word-Replacement Translation Models”, 1999, Computational Linguistics,25(4).
Knight, Kevin, ““Integrating Knowledge Acquisition and Language Acquisition””, May 1992, Journal of Applied Intelligence, vol. 1, No. 4.
Knight, Kevin, “Learning Word Meanings by Instruction,” 1996, Proc. of the D National Conference on Artificial Intelligence, vol. 1, pp. 447-454.
Knight, Kevin, “Unification: A Multidisciplinary Survey,” 1989, ACM Computing Surveys, vol. 21, No. 1.
Koehn, P. and Knight, K., “ChunkMT: Statistical Machine Translation with Richer Linguistic Knowledge,” Apr. 2002, Information Sciences Institution.
Koehn, P. and Knight, K., “Knowledge Sources for Word-Level Translation Models,” 2001, Conference on Empirical Methods in Natural Language Processing.
Koehn, P., et al, “Statistical Phrase-Based Translation,” Proceedings of HLT-NAACL 2003 Main Papers , pp. 48-54 Edmonton, May-Jun. 2003.
Abney, S.P., “Stochastic Attribute Value Grammars”, Association for Computional Linguistics, 1997, pp. 597-618.
Fox, H., “Phrasal Cohesion and Statistical Machine Translation” Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, Jul. 2002, pp. 304-311. Association for Computational Linguistics. <URL: http://acl.ldc.upenn.edu/W/W02/W02-1039.pdf>.
Tillman, C., et al, “Word Reordering and a Dynamic Programming Beam Search Algorithm for Statistical Machine Translation” <URL: http://acl.ldc.upenn.edu/J/J03/J03-1005.pdf>, 2003.
Wang, W., et al. “Capitalizing Machine Translation” In HLT-NAACL '06 Proceedings Jun. 2006. <http://www.isi.edu/natural-language/mt/hlt-naacl-06-wang.pdf>.
Langlais, P. et al., “TransType: a Computer-Aided Translation Typing System” EmbedMT '00 ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems, 2000, pp. 46-51. <http://acl.ldc.upenn.edu/W/W00/W00-0507.pdf>.
“Abney, Steven P. , ““Parsing by Chunks,”” 1991, Principle-Based Parsing: Computation and Psycholinguistics, vol. 44,pp. 257-279.”
Agbago, A., et al., “True-casing for the Portage System,” In Recent Advances in Natural Language Processing (Borovets, Bulgaria), Sep. 21-23, 2005, pp. 21-24.
Al-Onaizan et al., “Statistical Machine Translation,” 1999, JHU Summer Tech Workshop, Final Report, pp. 1-42.
“Al-Onaizan et al., ““Translating with Scarce Resources,”” 2000, 17th National Conference of the American Associationfor Artificial Intelligence, Austin, TX, pp. 672-678.”
Al-Onaizan, Y. and Knight K., “Machine Transliteration of Names in Arabic Text,” Proceedings of ACL Workshop on Computational Approaches to Semitic Languages. Philadelphia, 2002.
“Al-Onaizan, Y. and Knight, K., ““Named Entity Translation: Extended Abstract””, 2002, Proceedings of HLT-02, SanDiego, CA.”
“Al-Onaizan, Y. and Knight, K., ““Translating Named Entities Using Monolingual and Bilingual Resources,”” 2002, Proc. of the 40th Annual Meeting of the ACL, pp. 400-408.”
“Alshawi et al., ““Learning Dependency Translation Models as Collections of Finite-State Head Transducers,”” 2000, Computational Linguistics, vol. 26, pp. 45-60.”
Alshawi, Hiyan, “Head Automata for Speech Translation”, Proceedings of the ICSLP 96, 1996, Philadelphia, Pennslyvania.
Ambati, V., “Dependency Structure Trees in Syntax Based Machine Translation,” Spring 2008 Report <http://www.cs.cmu.edu/˜vamshi/publications/DependencyMT—report.pdf>, pp. 1-8.
“Arbabi et al., ““Algorithms for Arabic name transliteration,”” Mar. 1994, IBM Journal of Research and Development,vol. 38, Issue 2, pp. 183-194.”
Arun, A., et al., “Edinburgh System Description for the 2006 TC-STAR Spoken Language Translation Evaluation,” in TC-STAR Workshop on Speech-to-Speech Translation (Barcelona, Spain), Jun. 2006, pp. 37-41.
Ballesteros, L. et al., “Phrasal Translation and Query Expansion Techniques for Cross-Language Information,” SIGIR 97, Philadelphia, PA, © 1997, pp. 84-91.
“Bangalore, S. and Rambow, 0., ““Evaluation Metrics for Generation,”” 2000, Proc. of the 1st International NaturalLanguage Generation Conf., vol. 14, pp. 1-8.”
“Bangalore, S. and Rambow, 0., ““Using TAGs, a Tree Model, and a Language Model for Generation,”” May 2000,Workshop TAG+5, Paris.”
“Bangalore, S. and Rambow, O., ““Corpus-Based Lexical Choice in Natural Language Generation,”” 2000, Proc. of the 38th Annual ACL, Hong Kong, pp. 464-471.”
“Bangalore, S. and Rambow, O., ““Exploiting a Probabilistic Hierarchical Model for Generation,”” 2000, Proc. of 18thconf. on Computational Linguistics, vol. 1, pp. 42-48.”
Bannard, C. and Callison-Burch, C., “Paraphrasing with Bilingual Parallel Corpora,” In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (Ann Arbor, MI, Jun. 25-30, 2005). Annual Meeting of the ACL Assoc. for Computational Linguistics, Morristown, NJ, 597-604. DOI=http://dx.doi.org/10.3115/1219840.
“Barnett et al., ““Knowledge and Natural Language Processing,”” Aug. 1990, Communications of the ACM, vol. 33,Issue 8, pp. 50-71.”
“Baum, Leonard, ““An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Processes””, 1972, Inequalities 3:1-8.”
Berhe, G. et al., “Modeling Service-baed Multimedia Content Adaptation in Pervasive Computing,” CF '04 (Ischia, Italy) Apr. 14-16, 2004, pp. 60-69.
“Bikel et al., ““An Algorithm that Learns What's in a Name,”” 1999, Machine Learning Journal Special Issue on Natural Language Learning, vol. 34, pp. 211-232.”
Boitet, C. et al., “Main Research Issues in Building Web Services,” Proc. of the 6th Symposium on Natural Language Processing, Human and Computer Processing of Language and Speech, © 2005, pp. 1-11.
“Brants, Thorsten, ““TnT—A Statistical Part-of-Speech Tagger,”” 2000, Proc. of the 6th Applied Natural Language Processing Conference, Seattle.”
Brill, Eric, “Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging”, 1995, Assocation for Computational Linguistics, vol. 21, No. 4, pp. 1-37.
“Brill, Eric. ““Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging””,1995, Computational Linguistics, vol. 21, No. 4, pp. 543-565.”
“Brown et al., ““A Statistical Approach to Machine Translation,”” Jun. 1990, Computational Linguistics, vol. 16, No. 2, pp. 79-85.”
Brown et al., “Word-Sense Disambiguation Using Statistical Methods,” 1991, Proc. of 29th Annual ACL, pp. 264-270.
“Brown et al., ““The Mathematics of Statistical Machine Translation: Parameter D Estimation,”” 1993, Computational Linguistics, vol. 19, Issue 2, pp. 263-311.”
“Brown, Ralf, ““Automated Dictionary Extraction for ““Knowledge-Free”” Example-Based Translation,”” 1997, Proc. of 7th Int'l Cont. on Theoretical and Methodological Issues in MT, Santa Fe, NM, pp. 111-118.”
“Callan et al., ““TREC and TIPSTER Experiments with Inquery,”” 1994, Information Processing and Management, vol. 31, Issue 3, pp. 327-343.”
Callison-Burch, C. et al., “Statistical Machine Translation with Word- and Sentence-aligned Parallel Corpora,” In Proceedings of the 42nd Meeting on Assoc. for Computational Linguistics (Barcelona, Spain, Jul. 21-26, 2004) Annual Meeting of the ACL. Assoc. for Computational Linguistics, Morristown, NJ, 1.
“Carl, Michael. ““A Constructivist Approach to Machine Translation,”” 1998, New Methods of Language Processing and Computational Natural Language Learning, pp. 247-256.”
“Chen, K. and Chen, H., ““Machine Translation: An Integrated Approach,”” 1995, Proc. of 6th Int'l Cont. on Theoretical and Methodological Issue in MT, pp. 287-294.”
Cheng, P. et al., “Creating Multilingual Translation Lexicons with Regional Variations Using Web Corpora,” In Proceedings of the 42nd Annual Meeting on Assoc. for Computational Linguistics (Barcelona, Spain, Jul. 21-26, 2004). Annual Meeting of the ACL. Assoc. for Computational Linguistics, Morristown, NJ, 53.
Cheung et al., “Sentence Alignment in Parallel, Comparable, and Quasi-comparable Corpora”, In Proceedings of LREC, 2004, pp. 30-33.
Chinchor, Nancy, “MUC-7 Named Entity Task Definition,” 1997, Version 3.5.
“Clarkson, P. and Rosenfeld, R., ““Statistical Language Modeling Using the CMU-Cambridge Toolkit””, 1997, Proc. ESCA Eurospeech, Rhodes, Greece, pp. 2707-2710.”
Cohen et al., “Spectral Bloom Filters,” SIGMOD 2003, Jun. 9-12, 2003, ACM pp. 241-252.
Cohen, “Hardware-Assisted Algorithm for Full-text Large-dictionary String Matching Using n-gram Hashing,” 1998, Information Processing and Management, vol. 34, No. 4, pp. 443-464.
Cohen, Yossi, “Interpreter for FUF,” (available at ftp:/lftp.cs.bgu.acil/ pUb/people/elhadad/fuf-life.lf), 2008.
“Corston-Oliver, Simon, ““Beyond String Matching and Cue Phrases: Improving Efficiency and Coverage in Discourse Analysis””, 1998, The AAAI Spring Symposium on Intelligent Text Summarization, pp. 9-15.”
Covington, “An Algorithm to Align Words for Historical Comparison”, Computational Linguistics, 1996, 22(4), pp. 481-496.
“Dagan, I. and Itai, A., ““Word Sense Disambiguation Using a Second Language Monolingual Corpus””, 1994, Association for Computational Linguistics, vol. 20, No. 4, pp. 563-596.”
“Dempster et al., ““Maximum Likelihood from Incomplete Data via the EM Algorithm””, 1977, Journal of the Royal Statistical Society, vol. 39, No. 1, pp. 1-38.”
“Diab, M. and Finch, S., ““a Statistical Word-Level Translation Model for Comparable Corpora,”” 2000, In Proc. of the Conference on Content Based Multimedia Information Access (RIAO).”
“Diab, Mona, ““An Unsupervised Method for Multilingual Word Sense Tagging Using Parallel Corpora: A Preliminary Investigation””, 2000, SIGLEX Workshop on Word Senses and Multi-Linguality, pp. 1-9.”
Eisner, Jason,“Learning Non-Isomorphic Tree Mappings for Machine Translation,” 2003, in Proc. of the 41st Meeting of the ACL, pp. 205-208.
Elhadad et al., “Floating Constraints in Lexical Choice”, 1996, ACL, 23(2): 195-239.
“Elhadad, M. and Robin, J., ““An Overview of SURGE: a Reusable Comprehensive Syntactic Realization Component,”” 1996, Technical Report 96-03, Department of Mathematics and Computer Science, Ben Gurion University, Beer Sheva, Israel.”
“Koehn, P. and Knight, K., ““Estimating Word Translation Probabilities from Unrelated Monolingual Corpora Using the EM Algorithm,”” 2000, Proc. of the 17th meeting of the AAAI.”
“Kumar, R. and Li, H., ““Integer Programming Approach to Printed Circuit Board Assembly Time Optimization,”” 1995,IEEE Transactions on Components, Packaging, and Manufacturing, Part B: Advance Packaging, vol. 18,No. 4. pp. 720-727.”
Kupiec, Julian, “An Algorithm for Finding Noun Phrase Correspondecnes in Bilingual Corpora,” In Proceedings of the 31st Annual Meeting of the ACL, 1993, pp. 17-22.
“Kurohashi, S. and Nagao, M., ““Automatic Detection of Discourse Structure by Checking Surface Information in Sentences,”” 1994, Proc. of COL-LING '94, vol. 2, pp. 1123-1127.”
“Langkilde, I. and Knight, K., ““Generation that Exploits Corpus-Based Statistical Knowledge,”” 1998, Proc. of the COLING-ACL, pp. 704-710.”
“Langkilde, I. and Knight, K., ““The Practical Value of N-Grams in Generation,”” 1998, Proc. of the 9th International Natural Language Generation Workshop, pp. 248-255.”
“Langkilde, Irene, ““Forest-Based Statistical Sentence Generation,”” 2000, Proc. of the 1st Conference on North American chapter of the ACL, Seattle, WA, pp. 170-177.”
“Langkilde-Geary, Irene, ““A Foundation for General-Purpose Natural Language Generation: Sentence Realization Using Probabilistic Models of Language,”” 2002, Ph.D. Thesis, Faculty of the Graduate School, University of Southern California.”
“Langkilde-Geary, Irene, ““An Empirical Verification of Coverage and Correctness for a General-Purpose Sentence Generator,”” 1998, Proc. 2nd Int'l Natural Language Generation Conference.”
“Lee-Y.S., ““Neural Network Approach to Adaptive Learning: with an Application to Chinese Homophone Disambiguation,”” IEEE pp. 1521-1526.”, 2003.
Lita, L., et al., “tRuEcasing,” Proceedings of the 41st Annual Meeting of the Assoc. for Computational Linguistics (In Hinrichs, E. and Roth, D.—editors), pp. 152-159, 2003.
Llitjos, A. F. et al., “The Translation Correction Tool: English-Spanish User Studies,” Citeseer © 2004, downloaded from: http://gs37.sp.cs.cmu.edu/ari/papers/lrec04/fontll, pp. 1-4.
“Mann, G. and Yarowsky, D., ““Multipath Translation Lexicon Induction via Bridge Languages,”” 2001, Proc. of the2nd Conference of the North American Chapter of the ACL, Pittsburgh, PA, pp. 151-158.”
“Manning, C. and Schutze, H., ““Foundations of Statistical Natural Language Processing,”” 2000, The MIT Press, Cambridge, MA [redacted].”
“Marcu, D. and Wong, W., ““A Phrase-Based, Joint Probability Model for Statistical Machine Translation,”” 2002, Proc. of ACL-2 conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 133-139.”
“Marcu, Daniel, ““Building Up Rhetorical Structure Trees,”” 1996, Proc. of the National Conference on Artificial Intelligence and Innovative Applications of Artificial Intelligence Conference, vol. 2, pp. 1069-1074.”
“Marcu, Daniel, ““Discourse trees are good indicators of importance in text,”” 1999, Advances in Automatic Text Summarization, The MIT Press, Cambridge, MA.”
“Marcu, Daniel, ““Instructions for Manually Annotating the Discourse Structures of Texts,”” 1999, Discourse Annotation, pp. 1-49.”
“Marcu, Daniel, ““The Rhetorical Parsing of Natural Language Texts,”” 1997, Proceedings of Aclieacl '97, pp. 96-103.”
“Marcu, Daniel, ““The Rhetorical Parsing, Summarization, and Generation of Natural Language Texts,”” 1997, Ph. D. Thesis, Graduate Department of Computer Science, University of Toronto.”
“Marcu, Daniel, ““Towards a Unified Approach to Memory- and Statistical-Based Machine Translation,”” 2001, Proc. of the 39th Annual Meeting of the ACL, pp. 378-385.”
McCallum, A. and Li, W., “Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-enhanced Lexicons,” In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL, 2003, vol. 4 (Edmonton, Canada), Assoc. for Computational Linguistics, Morristown, NJ, pp. 188-191.
McDevitt, K. et al., “Designing of a Community-based Translation Center,” Technical Report TR-03-30, Computer Science, Virginia Tech, © 2003, pp. 1-8.
“Melamed, I. Dan, ““A Word-to-Word Model of Translational Equivalence,”” 1997, Proc. of the 35th Annual Meeting of the ACL, Madrid, Spain, pp. 490-497.”
“Melamed, I. Dan, ““Automatic Evaluation and Uniform Filter Cascades for Inducing N-Best Translation Lexicons,””1995, Proc. of the 3rd Workshop on Very Large Corpora, Boston, MA, pp. 184-198.”
“Melamed, I. Dan, ““Empirical Methods for Exploiting Parallel Texts,”” 2001, MIT Press, Cambridge, MA [table of contents].”
“Meng et al.. ““Generating Phonetic Cognates to Handle Named Entities in English-Chinese Cross-LanguageSpoken Document Retrieval,”” 2001, IEEE Workshop on Automatic Speech Recognition and Understanding. pp. 311-314.”
Metze, F. et al., “The NESPOLE! Speech-to-Speech Translation System,” Proc. of the HLT 2002, 2nd Int'l Conf. on Human Language Technology (San Francisco, CA), © 2002, pp. 378-383.
“Mikheev et al., ““Named Entity Recognition without Gazeteers,”” 1999, Proc. of European Chapter of the ACL, Bergen, Norway, pp. 1-8.”
“Miike et al., ““A full-text retrieval system with a dynamic abstract generation function,”” 1994, Proceedings of SI-GIR'94, pp. 152-161.”
“Mohri, M. and Riley, M., ““An Efficient Algorithm for the N-Best-Strings Problem,”” 2002, Proc. of the 7th Int. Conf. on Spoken Language Processing (ICSLP'02), Denver, CO, pp. 1313-1316.”
Mohri, Mehryar, “Regular Approximation of Context Free Grammars Through Transformation”, 2000, pp. 251-261, “Robustness in Language and Speech Technology”, Chapter 9, Kluwer Academic Publishers.
“Monasson et al., ““Determining computational complexity from characteristic ‘phase transitions’,”” Jul. 1999, Nature Magazine, vol. 400, pp. 133-137.”
“Mooney, Raymond, ““Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Biasin Machine Learning,”” 1996, Proc. of the Conference on Empirical Methods in Natural Language Processing, pp. 82-91.”
Nagao, K. et al., “Semantic Annotation and Transcoding: Making Web Content More Accessible,” IEEE Multimedia, vol. 8, Issue 2 Apr.-Jun. 2001, pp. 69-81.
“Nederhof, M. and Satta, G., ““IDL-Expressions: A Formalism for Representing and Parsing Finite Languages in Natural Language Processing,”” 2004, Journal of Artificial Intelligence Research, vol. 21, pp. 281-287.”
“Niessen,S. and Ney, H, ““Toward hierarchical models for statistical machine translation of inflected languages,”” 2001,Data-Driven Machine Translation Workshop, Toulouse, France, pp. 47-54.”
Norvig, Peter, “Techniques for Automatic Memoization with Applications to Context-Free Parsing”, Compuational Linguistics,1991, pp. 91-98, vol. 17, No. 1.
“Och et al., ““Improved Alignment Models for Statistical Machine Translation,”” 1999, Proc. of the Joint Conf. of Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 20-28.”
Och et al. “A Smorgasbord of Features for Statistical Machine Translation.” HLTNAACL Conference. Mar. 2004, 8 pages.
Och, F., “Minimum Error Rate Training in Statistical Machine Translation,” In Proceedings of the 41st Annual Meeting on Assoc. for Computational Linguistics—vol. 1 (Sapporo, Japan, Jul. 7-12, 2003). Annual Meeting of the ACL. Assoc. for Computational Linguistics, Morristown, NJ, 160-167. DOI= http://dx.doi.org/10.3115/1075096.
“Och, F. and Ney, H, ““Improved Statistical Alignment Models,”” 2000, 38th Annual Meeting of the ACL, Hong Kong, pp. 440-447.”
Och, F. and Ney, H., “Discriminative Training and Maximum Entropy Models for Statistical Machine Translation,” 2002, Proc. of the 40th Annual Meeting of the ACL, Philadelphia, PA, pp. 295-302.
Och, F. and Ney, H., “A Systematic Comparison of Various Statistical Alignment Models,” Computational Linguistics, 2003, 29:1, 19-51.
“Papineni et al., ““Bleu: a Method for Automatic Evaluation of Machine Translation,”” 2001, IBM Research Report, RC22176(WQ102-022).”
Perugini, Saviero et al., “Enhancing Usability in CITIDEL: Multimodal, Multilingual and Interactive Visualization Interfaces,” JCDL '04, Tucson, AZ, Jun. 7-11, 2004, pp. 315-324.
Petrov et al., “Learning Accurate, Compact and Interpretable Tree Annotation,” Jun. 4-9, 2006, in Proc. of the Human Language Technology Conference of the North American Chapter of the ACL, pp. 433-440.
“Pla et al., ““Tagging and Chunking with Bigrams,”” 2000, Proc. of the 18th Conference on Computational Linguistics vol. 2, pp. 614-620.”
“Rapp, Reinhard, Automatic Identification of Word Translations from Unrelated English and German Corpora, 1999,37th Annual Meeting of the ACL, pp. 519-526.”
“Rapp, Reinhard, ““Identifying Word Translations in Non-Parallel Texts,”” 1995, 33rd Annual Meeting of the ACL, pp. 320-322.”
Rayner et al.,“Hybrid Language Processing in the Spoken Language Translator,” IEEE, pp. 107-110, 1997.
“Resnik, P. and Smith, A., ““The Web as a Parallel Corpus,”” Sep. 2003, Computational Linguistics, Special Issue on Web as Corpus, vol. 29, Issue 3, pp. 349-380.”
“Resnik, P. and Yarowsky, D. ““A Perspective on Word Sense Disambiguation Methods and Their Evaluation,””1997, Proceedings of SIGLEX '97, Washington, D.C., pp. 79-86.”
“Resnik, Philip, ““Mining the Web for Bilingual Text,”” 1999, 37th Annual Meeting of the ACL, College Park, MD, pp. 527-534.”
Rich, E. and Knight, K., “Artificial Intelligence, Second Edition,” 1991, McGraw-Hili Book Company [redacted].
“Richard et al., ““Visiting the Traveling Salesman Problem with Petri nets and application in the glass industry,”” Feb. 1996, IEEE Emerging Technologies and Factory Automation, pp. 238-242.”
“Robin, Jacques, ““Revision-Based Generation of Natural Language Summaries Providing Historical Background: Corpus-Based Analysis, Design Implementation and Evaluation,”” 1994, Ph.D. Thesis, Columbia University, New York.”
Rogati et al., “Resource Selection for Domain-Specific Cross-Lingual IR,” ACM 2004, pp. 154-161.
Ruiqiang, Z. et al., “The NiCT-ATR Statistical Machine Translation System for the IWSLT 2006 Evaluation,” submitted to IWSLT, 2006.
“Russell, S. and Norvig, P., ““Artificial Intelligence: A Modern Approach,”” 1995, Prentice-Hall, Inc., New Jersey [redacted—table of contents].”
“Sang, E. and Buchholz, S., ““Introduction to the CoNLL-2000 Shared Task: Chunking,”” 20002, Proc. ofCoNLL-2000 and LLL-2000, Lisbon, Portugal, pp. 127-132.”
Schmid, H., and Schulte im Walde, S., “Robust German Noun Chunking With a Probabilistic Context-Free Grammar,” 2000, Proc. of the 18th Conference on Computational Linguistics, vol. 2, pp. 726-732.
“Schutze, Hinrich, ““Automatic Word Sense Discrimination,”” 1998, Computational Linguistics, Special Issue on Word Sense Disambiguation, vol. 24, Issue 1, pp. 97-123.”
“Selman et al., ““A New Method for Solving Hard Satisfiability Problems,”” 1992, Proc. of the 10th National Conference on Artificial Intelligence, San Jose, CA, pp. 440-446.”
Kumar, S. and Byrne, W., “Minimum Bayes-Risk Decoding for Statistical Machine Translation.” HLTNAACL Conference. Mar. 2004, 8 pages.
“Shapiro, Stuart (ed.), ““Encyclopedia of Artificial Intelligence, 2nd edition””, vol. D 2,1992, John Wiley & Sons Inc;““Unification”” article, K. Knight, pp. 1630-1637.”
Shirai, S., “A Hybrid Rule and Example-based Method for Machine Translation,” NTT Communication Science Laboratories, pp. 1-5, 1997.
“Sobashima et al., ““A Bidirectional Transfer-Driven Machine Translation System for Spoken Dialogues,”” 1994, Proc. of 15th Conference on Computational Linguistics, vol. 1, pp. 64-68.”
“Soricut et al., ““Using a large monolingual corpus to improve translation accuracy,”” 2002, Lecture Notes in Computer Science, vol. 2499, Proc. of the 5th Conference of the Association for Machine Translation in theAmericas on Machine Translation: From Research to Real Users, pp. 155-164.”
“Sumita et al., ““A Discourse Structure Analyzer for Japanese Text,”” 1992, Proc. of the International Conference on Fifth Generation Computer Systems, vol. 2, pp. 1133-1140.”
“Sun et al., ““Chinese Named Entity Identification Using Class-based Language Model,”” 2002, Proc. of 19th International Conference on Computational Linguistics, Taipei, Taiwan, vol. 1, pp. 1-7.”
Tanaka, K. and Iwasaki, H. “Extraction of Lexical Translations from Non-Aligned Corpora,” Proceedings of COLING 1996.
Taskar, B., et al., “A Discriminative Matching Approach to Word Alignment,” In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (Vancouver, BC, Canada, Oct. 6-8, 2005). Human Language Technology Conference. Assoc. for Computational Linguistics, Morristown, NJ.
“Taylor et al., ““The Penn Treebank: An Overview,”” in A. Abeill (ed.), D Treebanks: Building and Using Parsed Corpora, 2003, pp. 5-22.”
“Tiedemann, Jorg, ““Automatic Construction of Weighted String Similarity Measures,”” 1999, In Proceedings ofthe Joint SIGDAT Conference on Emperical Methods in Natural Language Processing and Very Large Corpora.”
“Tillman, C. and Xia, F., ““A Phrase-Based Unigram Model for Statistical Machine Translation,”” 2003, Proc. of the North American Chapter of the ACL on Human Language Technology, vol. 2, pp. 106-108.”
“Tillmann et al., ““A DP based Search Using Monotone Alignments in Statistical Translation,”” 1997, Proc. of the Annual Meeting of the ACL, pp. 366-372.”
Tomas, J., “Binary Feature Classification for Word Disambiguation in Statistical Machine Translation,” Proceedings of the 2nd Int'l. Workshop on Pattern Recognition, 2002, pp. 1-12.
Stalls, B. and Knight, K., “Translating Names and Technical Terms in Arabic Text,” 1998, Proc. of the COLING/ACL Workkshop on Computational Approaches to Semitic Language.
“Ueffing et al., ““Generation of Word Graphs in Statistical Machine Translation,”” 2002, Proc. of Empirical Methods in Natural Language Processing (EMNLP), pp. 156-163.”
Varga et al, “Parallel corpora for medium density languages”, In Proceedings of RANLP 2005, pp. 590-596.
“Veale, T. and Way, A., ““Gaijin: A Bootstrapping, Template-Driven Approach to Example-Based MT,”” 1997, Proc. of New Methods in Natural Language Processing (NEMPLP97), Sofia, Bulgaria.”
Vogel et al., “The CMU Statistical Machine Translation System,” 2003, Machine Translation Summit IX, New Orleans, LA.
“Vogel et al., ““The Statistical Translation Module in the Verbmobil System,”” 2000, Workshop on Multi-Lingual Speech Communication, pp. 69-74.”
“Vogel, S. and Ney, H., ““Construction of a Hierarchical Translation Memory,”” 2000, Proc. of Cooling 2000, Saarbrucken, Germany, pp. 1131-1135.”
“Wang, Y. and Waibel, A., ““Decoding Algorithm in Statistical Machine Translation,”” 1996, Proc. of the 35th Annual Meeting of the ACL, pp. 366-372.”
“Wang, Ye-Yi, ““Grammar Inference and Statistical Machine Translation,”” 1998, Ph.D Thesis, Carnegie Mellon University, Pittsburgh, PA.”
“Watanabe et al., ““Statistical Machine Translation Based on Hierarchical Phrase Alignment,”” 2002, 9th International Conference on Theoretical and Methodological Issues in Machin Translation (TMI-2002), Keihanna, Japan, pp. 188-198.”
“Witbrock, M. and Mittal, V., ““Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries,”” 1999, Proc. of SIGIR '99, 22nd International Conference on Research and Development in Information Retrieval, Berkeley, CA, pp. 315-316.”
“Wu, Dekai, ““A Polynomial-Time Algorithm for Statistical Machine Translation,”” 1996, Proc. of 34th Annual Meeting of the ACL, pp. 152-158.”
“Wu, Dekai, ““Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora,”” 1997, Computational Linguistics, vol. 23, Issue 3, pp. 377-403.”
“Yamada, K. and Knight, K. ““A Syntax-based Statistical Translation Model,”” D 2001, Proc. of the 39th Annual Meeting of the ACL, pp. 523-530.”
“Yamada, K. and Knight, K., ““A Decoder for Syntax-based Statistical MT,”” 2001, Proceedings of the 40th Annual Meeting of the ACL, pp. 303-310.”
Yamada K., “A Syntax-Based Statistical Translation Model,” 2002 PhD Dissertation, pp. 1-141.
“Yamamoto et al., ““A Comparative Study on Translation Units for Bilingual Lexicon Extraction,”” 2001, Japan Academic Association for Copyright Clearance, Tokyo, Japan.”
Yamamoto et al, “Acquisition of Phrase-level Bilingual Correspondence using Dependency Structure” In Proceedings of COLING-2000, pp. 933-939.
“Yarowsky, David, ““Unsupervised Word Sense Disambiguation Rivaling Supervised Methods,”” 1995, 33rd Annual Meeting of the ACL, pp. 189-196.”
Zhang et al., “Synchronous Binarization for Machine Translations,” Jun. 4-9, 2006, in Proc. of the Human Language Technology Conference of the North American Chapter of the ACL, pp. 256-263.
Huang et al. Automatic Extraction of Named Entity Translingual Equivalence Based on Multi-Feature Cost Minimization. In Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-Language Name Entry Recognition.
Bangalore, S. and Rambow, O., “Using TAGs, a Tree Model, and a Language Model for Generation,” May 2000, Workshop TAG+5, Paris.
Gale, W. and Church, K., “A Program for Aligning Sentences in Bilingual Corpora,” 1993, Computational Linguistics, vol. 19, No. 1, pp. 75-102.
Bangalore, S. and Rambow, O., ““Using TAGs, a Tree Model, and a Language Model for Generation,”” May 2000,Workshop TAG+5, Paris.
Ueffing et al., “Using POS Information for Statistical Machine Translation into Morphologically Rich Languages,” In EACL, 2003: Proceedings of the Tenth Conference on European Chapter of the Association for Computational Linguistics, pp. 347-354.
Papineni et al., “Bleu: a Method for Automatic Evaluation of Machine Translation”, Proc. of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Jul. 2002, pp. 311-318.
Shaalan et al., “Machine Translation of English Noun Phrases into Arabic”, (2004), vol. 17, No. 2, International Journal of Computer Processing of Oriental Languages, 14 pages.
Isahara et al., “Analysis, Generation and Semantic Representation in Contrast—A Context-Based Machine Translation System”, 1995, Systems and Computers in Japan, vol. 26, No. 14, pp. 37-53.
Proz.com, Rates for proofreading versus Translating, http://www.proz.com/forum/business—issues/202-rates—for—proofreading—versus—translating.html, Apr. 23, 2009, retrieved Jul. 13, 2012.
Celine, Volume discounts on large translation project, naked translations, http://www.nakedtranslations.com/en/2007/volume-discounts-on-large-translation-projects/, Aug. 1, 2007, retrieved Jul. 16, 2012.
Graehl, J and Knight, K, May 2004, Training Tree Transducers, In NAACL-HLT (2004), pp. 105-112.
Niessen et al, “Statistical machine translation with scarce resources using morphosyntactic information”, Jun. 2004, Computational Linguistics, vol. 30, issue 2, pp. 181-204.
Liu et al., “Context Discovery Using Attenuated Bloom Filters in Ad-Hoc Networks,” Springer, pp. 13-25, 2006.
First Office Action mailed Jun. 7, 2004 in Canadian Patent Application 2408819, filed May 11, 2001.
First Office Action mailed Jun. 14, 2007 in Canadian Patent Application 2475857, filed Mar. 11, 2003.
Office Action mailed Mar. 26, 2012 in German Patent Application 10392450.7, filed Mar. 28, 2003.
First Office Action mailed Nov. 5, 2008 in Canadian Patent Application 2408398, filed Mar. 27, 2003.
Second Office Action mailed Sep. 25, 2009 in Canadian Patent Application 2408398, filed Mar. 27, 2003.
First Office Action mailed Mar. 1, 2005 in European Patent Application No. 03716920.8, filed Mar. 27, 2003.
Second Office Action mailed Nov. 9, 2006 in European Patent Application No. 03716920.8, filed Mar. 27, 2003.
Third Office Action mailed Apr. 30, 2008 in European Patent Application No. 03716920.8, filed Mar. 27, 2003.
Office Action mailed Oct. 25, 2011 in Japanese Patent Application 2007-536911 filed Oct. 12, 2005.
Office Action mailed Jul. 24, 2012 in Japanese Patent Application 2007-536911 filed Oct. 12, 2005.
Final Office Action mailed Apr. 9, 2013 in Japanese Patent Application 2007-536911 filed Oct. 12, 2005.
Office Action mailed May 13, 2005 in Chinese Patent Application 1812317.1, filed May 11, 2001.
Office Action mailed Apr. 21, 2006 in Chinese Patent Application 1812317.1, filed May 11, 2001.
Office Action mailed Jul. 19, 2006 in Japanese Patent Application 2003-577155, filed Mar. 11, 2003.
Office Action mailed Mar. 1, 2007 in Chinese Patent Application 3805749.2, filed Mar. 11, 2003.
Office Action mailed Feb. 27, 2007 in Japanese Patent Application 2002-590018, filed May 13, 2002.
Office Action mailed Jan. 26, 2007 in Chinese Patent Application 3807018.9, filed Mar. 27, 2003.
Office Action mailed Dec. 7, 2005 in Indian Patent Application 2283/DELNP/2004, filed Mar. 11, 2003.
Office Action mailed Mar. 31, 2009 in European Patent Application 3714080.3, filed Mar. 11, 2003.
Agichtein et al., “Snowball: Extracting Information from Large Plain-Text Collections,” ACM DL '00, the Fifth ACM Conference on Digital Libraries, Jun. 2, 2000, San Antonio, TX, USA.
Satake, Masaomi, “Anaphora Resolution for Named Entity Extraction in Japanese Newspaper Articles,” Master's Thesis [online], Feb. 13, 2002, School of Information Science, JAIST, Nomi, Ishikaw, Japan.
Office Action mailed Aug. 29, 2006 in Japanese Patent Application 2003-581064, filed Mar. 27, 2003.
Office Action mailed Jan. 26, 2007 in Chinese Patent Application 3807027.8, filed Mar. 28, 2003.
Office Action mailed Jul. 25, 2006 in Japanese Patent Application 2003-581063, filed Mar. 28, 2003.
Huang et al., “A syntax-directed translator with extended domain of locality,” Jun. 9, 2006, In Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing, pp. 1-8, New York City, New York, Association for Computational Linguistics.
Melamed et al., “Statistical machine translation by generalized parsing,” 2005, Technical Report 05-001, Proteus Project, New York University, http://nlp.cs.nyu.edu/pubs/.
Galley et al., “Scalable Inference and Training of Context-Rich Syntactic Translation Models,” Jul. 2006, In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pp. 961-968.
Huang et al., “Statistical syntax-directed translation with extended domain of locality,” Jun. 9, 2006, In Proceedings of AMTA, pp. 1-8.
Related Publications (1)
Number Date Country
20080270109 A1 Oct 2008 US
Provisional Applications (1)
Number Date Country
60562774 Apr 2004 US
Divisions (1)
Number Date Country
Parent 11107304 Apr 2005 US
Child 12132401 US