TREA

Multi-lingual online community

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Information

  • Patent Grant
  • 8468149
  • Patent Number
    8,468,149
  • Date Filed
    Friday, January 26, 2007
    17 years ago
  • Date Issued
    Tuesday, June 18, 2013
    11 years ago
Information
Abstract
A method, system and computer readable medium for hosting a community in multiple languages is disclosed. The method comprises receiving a request from a first member of a community to post content within the community. The content is written in a first language. Next, the content is translated into a second language. Login information is received from a second member. The second member may be associated with a second language. Lastly, the translated content is displayed to the second member. The method may further comprise receiving feedback from the second member relating to the quality of the translation or post edited versions of the automatically produced translation.
Description
BACKGROUND

1. Field of the Present Invention


This application relates generally to online communities and more specifically to providing a multi-lingual online community.


2. Related Art


Currently, the Internet, including the World-Wide Web (WWW), is used to publish material written in many languages. This material includes news articles, essays, blog posts, community discussion groups, business and shopping websites, and the like. In some instances, this material is posted in multiple languages. For example, an international hotel chain may publish multiple versions of a website in languages in which its guests are likely to communicate. For example, a hotel chain in Europe may post the same website in English, French, German, Italian, Spanish, and Romanian. When a guest accesses the website, the guest may be given an opportunity to select a preferred language. The hotel website may then download a “cookie” to the guest's computer indicating the preferred language so that when the guest subsequently visits the website, the corresponding version of the website is automatically displayed.


The Internet has further enabled people to meet and communicate with one another in online communities. Online communities, such as Digg, Deli.ci.ous, StumbledUpon, and Ars Technica enable members to provide content in the form of original articles and posts within discussion groups. Further, a member may provide a link to material posted by a third party. Other members may, in turn, comment on the material. For example, a member may provide a link to a blog posted on the Internet. After reading the material, another member may post a comment in reaction to the article. Further, other members may post additional comments in reaction to the comments posted by the members. In some communities, members can vote on or rate the articles posted in the communities.


In online communities, all of the members typically need to speak the same language in order to have meaningful discussions. Currently, this condition results in online communities that are segregated based on language. For example, an English speaker can not meaningfully participate in an Arabic-speaking online community and vice-versa. Further, membership in online communities is limited to members who speak the language of the community.


SUMMARY

A method for hosting a community in multiple languages is provided. The method comprises receiving a request from a first member of a community. The request indicates an instruction to post content written in a first language within the community. Next, the content is translated into a second language. Login information is received from a second member. The second member is associated with the second language. The translated content is displayed to the second member.


The method may further comprise retrieving the content from a content source. The second member may be associated with the second language via a profile. The content may comprise a post in a message board associated with the community or a comment associated with the community.


The method may further comprise providing a link to the content in the first language to the second member and the content may be displayed to the second member in the first language. Feedback may be received from the second member. The feedback may relate to the translation of the content or include a suggested translation.


A system for hosting a community in multiple languages may comprise a content module, a translation module, and a storage medium. The content module may receive a request from a first member of a community to post content in a first language within the community. The translation module may translate the content into a second language. The storage medium may store the translated content.


A computer readable medium having embodied thereon a program, the program being executable by a processor for performing a method for hosting a community in multiple languages is provided. The method comprises receiving a request from a first member of a community. The request indicates an instruction to post content written in a first language within the community. Next, the content is translated into a second language. Login information is received from a second member. The second member is associated with the second language. The translated content is displayed to the second member.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 depicts an exemplary environment in which various embodiments of the invention may be practiced;



FIG. 2 depicts a block diagram of an exemplary community host according to various embodiments;



FIG. 3 depicts an exemplary screenshot of an online community in English according to various embodiments;



FIG. 4 depicts an exemplary screenshot of the online community in French according to various embodiments;



FIG. 5 depicts a flowchart illustrating an exemplary method for hosting a multi-lingual online community according to various embodiments; and



FIG. 6 depicts a flowchart illustrating an exemplary method for receiving translation feedback according to various embodiments.





DETAILED DESCRIPTION

An exemplary method, system, and computer readable medium for hosting a multi-lingual community are provided. The multi-lingual community is an online community wherein a significant portion of content posted in the online community is translated into at least one language other than the language in which the content was originally posted. The multilingual community allows members who are otherwise unable to communicate with each other due to language barriers to each communicate in a preferred language in substantially real time. The multilingual community may include content generated by the members, such as posts within discussion groups, original articles, comments posted by the members relating to other content, and/or any type of media.



FIG. 1 depicts an exemplary environment 100 in which various embodiments of the invention may be practiced. The environment 100 may comprise a community host 102, one or more content sources 106A through 106N, and one or more members 108A through 108N coupled via a network 110. The community host 102 may communicate with an external translation engine 104.


The community host 102 may host a website comprising an online community. The online community includes content selected and/or generated by the members 108A and 108N. The community host 102 may generate the website comprising the content associated with the online community. The community host 102 may include profiles of the members 108A through 108N indicating demographic information and/or various preferences, including a preferred language. The community host 102 may further include an interface allowing a user, such as the members 108A through 108N, to remove, edit, or add content to the online community. The community host 102 may further provide translations of the content via the translation engine 104.


The translation engine 104 is depicted separately from the community host 102 in FIG. 1. In other embodiments, and as described in FIG. 2, the translation engine 104 may be embodied in a module within the community host 102. The translation engine 104 may comprise a statistical machine translation (SMT) engine. The SMT engine generates a translation of a document based on a set of statistical probabilities associated with a source language of the document and a target language into which the document is translated. The translation engine 104, in other embodiments, may generate a translation according to a Translation Memory (TM) database comprising entries containing parallel translations of words and/or phrases in at least two languages.


The content sources 106A through 106N comprise websites typically not associated with the online community. However, one or more of the content sources 106A through 106N may be associated with the online community according to some embodiments. For example, the content source 106A may comprise an online newspaper, blog, a website associated with a business, or the like. The content source 106A, for example, may provide content in a plurality of languages. For example, a news website may post articles in English, French, Arabic, and Japanese.


The members 108A through 108N comprise users who are associated with the online community hosted by the community host 102. The members 108A through 108N may speak different languages. A member, such as the member 108A, may select content posted by the content source 106A in any language. Content in languages supported by the community host 102 may be translated into the other languages supported by the community host 102 and posted within the online community. The content may be posted within the online community by providing a link to the website of the content source 106A. In instances where the content appears in multiple languages at the content source 106A, links may be provided within the online community accordingly. The community host 102 may also generate a mirror copy of the content.


Each member, such as the member 108A, may generate content within the online community. For example, the member 108A may write an original article and post the article in the online community. The member 108A may also generate content by participating in a discussion group or by commenting on an article posted by another member, such as the member 108N.


In operation, the community host 102 receives the content and/or the content selections from the members 108A through 108N. The community host 102 posts the content and/or a link to the content within the online community. The translation engine 104 may associate the content with a source language and translate the content into at least one other target language. For example, the translation engine 104 may translate a copy of the content (e.g., a news article) selected by the member 108A. The translation engine 104 further translates the content generated by the members 108A through 108N of the online community. As such, if the member 108A logs into the online community, the member 108A is able to view the content or link to the content posted in the online community in a preferred language associated with the member 108A.



FIG. 2 depicts a block diagram of an exemplary community host 102 according to various embodiments. The community host 102 may comprise a content module 202, a translation module 204, an interface module 206, and a profile module 208. In exemplary embodiments, the community host 102 generates and maintains the online community.


The exemplary content module 202 retrieves and stores the content posted in the online community. The content module 202 may receive a request from a first member of a community to post content in a first language within the community and store the content itself, one or more links to the content, and/or translated copies of the content in a database or other storage medium. The content module 202 retrieves and arranges documents from a content source, such as the content sources 106A through 106N, comments associated with the documents, and/or posts within a discussion group in a website associated with the online community.


The translation module 204 comprises software, firmware, and/or hardware for translating the content into one or more languages. The translation module 204 may be based on SMT and/or TM. The translation module 204 may further generate language-specific versions of the online community. The translation module 204 is configured to generate translations of the user-selected and/or user-generated content within the community into at least one other language. The translation module 204 allows the members 108A through 108N to communicate with each other in each member's preferred language.


The interface module 206 generates a website or other interface for members 108A through 108N to view and add content to the online community. The interface module 206 may receive login information from a member 108A associated with a second language and display the translated content to the member 108A. To receive the login information, the interface module 206 may display a login interface to the member 108A. The member 108A may provide a password and screen name to login to the online community.


The screen name and/or a member's name may be verified by a profile module 208. The profile module 208 processes information associated with the members 108A through 108N. The profile module 208 may access a stored profile associated with the member 108A. The profile module 208 may also maintain or update a database comprising information such as a member login name, preferred language, demographic information, or the like. The preferred language information may be used by the content module 202 and the interface module 206 to display the online community in an individual member's preferred language if the member has logged into the online community. For example, the member 108A may generate a profile within the profile module 208 indicating that the member's preferred language is Arabic. If the member 108A subsequently logs in to the community host 102 via the online community website, the community host 102 will automatically transmit the Arabic version of the content posted within the community.


Although the community host 102 in FIG. 2 is illustrated as being comprised of the content module 202, the translation module 204, the interface module 206, and the profile module 208, fewer or more modules may comprise the community host 102 and still fall within the scope of various embodiments.



FIG. 3 depicts an exemplary screenshot 300 of an online community in English according to various embodiments. The screenshot 300 is an exemplary depiction of a webpage where member 108A may post a comment relating to content posted within the online community. In exemplary embodiments, the screenshot 300 may comprise original content 302, menu items 304, and comments 306.


The original content 302 may comprise the content or a link to the content, a name or other identifier of the member 108A who posted the content, and/or an introduction to the content written by the member 108A. In various embodiments, the link may direct the member 108N to the original content posted by the content source 106A, a translation of the content posted by the content source 106A, or to a copy or translation of the content maintained by the community host 102. In some embodiments the link may be a text link.


In exemplary embodiments, the menu items 304 may comprise an option to edit the content, comment about the content, and/or make a suggestion regarding the content and/or a translation of the content. Further, the menu items 304 may include links to other translations of the content. For example, as shown, links to translations in Arabic, Chinese, French, and Spanish versions of the content and/or the comments 306 are provided.


The comments 306 comprise a listing of comments made by members 108A through 108N regarding the content. The comments 306 may further comprise a translation of a comment relating to the original content 302 posted by a member 108N. For example, the comment 306 may be translated to or from French.



FIG. 4 depicts an exemplary screenshot 400 of the online community in French according to various embodiments. The screenshot 400 is an exemplary depiction of a webpage where the member 108A may post a comment relating to content posted within the online community. The screenshot 400 depicts a French-language version of the screenshot 300 of FIG. 3.


Like the screenshot 300, the screenshot 400 may comprise translated original content 402, menu items 404, and comments 406. The translated original content 402 may comprise a link to a translation of the original content 302. Further, the translated original content 402 may include an indication of a quality of the translation. The menu items 404 may be displayed in English (as depicted) or in the same language that the content and/or the comments are displayed. In this embodiment, for example, the menu items 404 may be displayed in French. The comments 406 may be translated into French from other languages or received in French and posted within the online community.


For example, in the embodiments depicted in FIG. 3 and FIG. 4, the member “ralphy” may be a French speaker, the member “flungster” may be an English speaker, and the member “theman” may be a Chinese speaker. The member “flungster” may post the original content 302 in the online community. The community host 102 may link to and/or generate a translation of the original content 302. The members “ralphy” and “theman” may view the French and Chinese translated original content 402, respectively. Each member may additionally post comments in their language and read the comments posted by the other members regardless of the original language that the content and the comments were written in. In this example, “flungster” may view screenshot 300 and “ralphy” may view screenshot 400.



FIG. 5 depicts a flowchart illustrating a process 500 for hosting a multi-lingual online community according to exemplary embodiments. In exemplary embodiments, the process 500 may be performed by the community host 102. The process 500 allows the members 108A through 108N to communicate with each other regardless of whether the members 108A through 108N speak the same language. The process 500 may be performed for each piece of the content selected and/or generated by the members 108A through 108N.


In step 502, a request to post content within the online community is received from a first member, such as the member 108A. The content may, comprise an article, media, or document posted by a content source, such as the content source 106A, or content generated by the member 108A, for example. In embodiments where the content is posted by the content source 106A, the content may be retrieved from the content source 106A by the content module 202 in step 504. In some embodiments, the content may be retrieved by generating at least one link to the content source 106A and/or generating a copy of the content.


In instances where the content source 106A posts the content in more than one language, additional translations may also be retrieved from the content source 106A. For example, if the member 108A selects the content from a hotel website that is published in English, French, German, Spanish, Italian, and Romanian, the content in each of the five languages may be retrieved from the hotel website and/or separately linked to the online community.


In embodiments where the content is generated by the member 108A, the content module 202 receives the content from the member 108A. In some embodiments, the content module 202 may associate the content with the member 108A. The content generated by the member 108A may be also associated with content retrieved from the content source 106A.


In step 506, the content may be automatically translated into one or more languages. In exemplary embodiments, the translation engine 104 or the translation module 204 may translate the content using an SMT engine and/or by referring to a TM database. In alternative embodiments, other forms of translation engines and databases may be utilized. The content may be translated into more than one language depending on the languages supported within the online community and/or requested by the members 108A through 108N.


In step 508, a determination may be made based on whether another member, such as a second member, has logged into the online community. For example, the second member may log in via the interface module 206. This second member may be associated with a profile indicating a preferred language. In step 510, the preferred language information may be retrieved from a database in which the second member's profile is maintained by, for example, the profile module 208. In some embodiments, the second member may be associated with more than one preferred language. In alternative embodiments, the translation may be generated in response to a request for content in a preferred language. To illustrate, rather than performing step 506 once the content is retrieved, the translation into the preferred language may be generated after step 510 is performed.


In step 512, the content within the community is displayed to the second member in the second member's preferred language by the interface module 206. Links to content from the content sources 106A through 106N may be provided according to the preferred language such that an English speaker views a link to the English version while a French speaker views a link to the French version. The second member may further select and/or generate additional content to be posted within the online community in the second member's preferred language. The content posted by the second member may be translated into the first member's language such that if the first member logs into the online community, the first member may view the content generated by the second member in the first member's preferred language.


To illustrate, the first member may generate a post, in Arabic, in a discussion group within the online community. An English-speaking second member may view an English translation of the post. The English-speaking second member may then post a reply in the discussion group in English. In turn, the first member may view an Arabic translation of the English language reply. Additional members may read and reply to the posts in other languages. Thus, members are able to communicate with one another in each member's preferred language.



FIG. 6 depicts a flowchart illustrating a method 600 for receiving translation feedback according to exemplary embodiments. A member who is fluent in, or has knowledge of, two or more languages may wish to view the original content even though it is not written in the member's preferred language. A member may wish to view the content if, for example, the translation is confusing, the member wishes to refine his or her knowledge of the language, or for other reasons. According to exemplary embodiments, translation feedback may be used to train an SMT engine if the translation feedback is collected by the translation engine 104 or the translation module 204.


In step 602, a request to view the original content is received from a member, such as the member 108A. The original content is the content in the language in which it was generated and/or retrieved. The original content is displayed in step 604. In some embodiments, a feedback interface may also be displayed.


In step 606, quantitative feedback may be received from the member 108A via the feedback interface. The quantitative feedback may include a rating indicating translation quality. For example, the member 108A may rate a translation on a numerical scale of one to five where a rating of one indicates an inaccurate translation, a rating of three indicates an accurate, but linguistically awkward, translation, and a rating of five indicates that the translation is both accurate and fluent. Any rating system may be utilized to provide feedback about the translation and any meaning may be assigned to rating symbols. For example, a rating of one or a non-numerical symbol may indicate a perfect translation according to some embodiments.


In step 608, qualitative feedback may be received from the member 108A. Qualitative feedback may include a comment about a specific aspect of the translation such as tone, slang usage, and so forth. Qualitative feedback may additionally include suggested translations for words or phrases within the content. For example, the member 108A may suggest an alternative translation for jargon or slang phrases used in the posted content. In one embodiment, qualitative feedback may additionally include a fully post edited version of the translation made available by the online community server; or a post edited version of a translation that was previously post edited by another community member.


In step 610, a qualitative feedback comprising a post edited version of the translation may be published in the online community. The post edited version of the translation may be reviewed by a human translator or automatic process for accuracy. For example, the human translator may be the member 108N. A further post edited version of the translation, or other feedback, may be received from the member 108N in step 612. The post edited version of the translation, or other feedback, received from member 108N may be posted in the online community by returning to step 610. If one of the post edited versions of the translation is accurate, the accurate version of the translation may be added to a training set in the SMT engine and/or TM database. In some embodiments, no feedback is collected when the original content is displayed.


The above-described functions and components can be comprised of instructions that are stored on a storage medium. The instructions can be retrieved and executed by a processor. Some examples of instructions are software, program code, and firmware. Some examples of storage medium are memory devices, tape, disks, integrated circuits, and servers. The instructions are operational when executed by the processor to direct the processor to operate in accord with various embodiments. Those skilled in the art are familiar with instructions, processor(s), and storage medium.


While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. The scope of the present disclosure is in no way limited to the languages used to describe exemplary embodiments. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments.

Claims
  • 1. A method for hosting a community in multiple languages on a server, the method comprising: receiving a request at the server via a network from a first member of the community to post content in a first language within the community, the content including a text link in the first language;translating the content, including the text link in the first language, into a second language;receiving login information at the server via a network from a second member associated with the second language;displaying the translated content to the second member;providing a link to the content in the first language to the second member; andreceiving qualitative and quantitative feedback from the second member relating to the translation of the content, the feedback including a suggested translation,wherein the second member is associated with the second language via a profile.
  • 2. The method recited in claim 1, further comprising retrieving the content from a content source.
  • 3. The method recited in claim 1, wherein the content comprises a post in a message board associated with the community.
  • 4. The method recited in claim 1, wherein the content comprises a comment associated with the community.
  • 5. The method recited in claim 1, further comprising displaying the content in the first language to the second member.
  • 6. The method recited in claim 1, wherein translating the content is performed using a statistical machine translation (SMT) engine.
  • 7. The method recited in claim 6, further comprising training the SMT engine using the qualitative and quantitative feedback.
  • 8. The method recited in claim 1, wherein translating the content is performed using a translation memory (TM) database.
  • 9. The method recited in claim 1, further comprising generating a mirror copy of the content, wherein the mirror copy is in the second language.
  • 10. A system for hosting a community in multiple languages comprising: a content module configured to receive a request from a first member of the community to post content in a first language within the community, the content including a text link in the first language;a translation module configured to translate the content, including the text link in the first language, into a second language;an interface module configured to receive login information from a second member associated with the second language, display the translated content to the second member, provide a link to the content in the first language to the second member, and receive qualitative and quantitative feedback including a suggested translation from the second member relating to the translation of the content;a profile module configured to store a profile associated with the second member, the profile indicating the second language; anda storage medium configured to store the translated content.
  • 11. The system recited in claim 10, wherein the content module is further configured to retrieve the content from a content source.
  • 12. The system recited in claim 10, wherein the content comprises a post in a message board associated with the community.
  • 13. The system recited in claim 10, wherein the content comprises a comment associated with the community.
  • 14. The system recited in claim 10, wherein the interface module is further configured to display the content in the first language to the second member.
  • 15. The system recited in claim 10, wherein the translation module is further configured based on statistical machine translation (SMT).
  • 16. The system recited in claim 15, wherein the translation module is further configured to use the qualitative and quantitative feedback to train the SMT.
  • 17. The system recited in claim 10, wherein the translation module is further configured based on translation memory (TM).
  • 18. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor for performing a method for hosting a community in multiple languages, the method comprising: receiving a request from a first member of the community to post content in a first language within the community, the content including a text link in the first language;translating the content, including the text link in the first language, into a second language;receiving login information from a second member associated with the second language;displaying the translated content to the second member;providing a link to the content in the first language to the second member; andreceiving qualitative and quantitative feedback from the second member relating to the translation of the content, the feedback including a suggested translation,wherein the second member is associated with the second language via a profile.
  • 19. The non-transitory computer readable storage medium recited in claim 18, wherein the method further comprises retrieving the content from a content source.
  • 20. The non-transitory computer readable storage medium recited in claim 18, wherein the content comprises a post in a message board associated with the community.
  • 21. The non-transitory computer readable storage medium recited in claim 18, wherein the content comprises a comment associated with the community.
  • 22. The non-transitory computer readable storage medium recited in claim 18, wherein the method further comprises displaying the content in the first language to the second member.
  • 23. The non-transitory computer readable storage medium recited in claim 18, wherein translating the content is performed using a statistical machine translation (SMT) engine.
  • 24. The non-transitory computer readable storage medium recited in claim 23, wherein the method further comprises training the SMT engine using the qualitative and quantitative feedback.
  • 25. The non-transitory computer readable storage medium recited in claim 18, wherein translating the content is performed using a translation memory (TM) database.
US Referenced Citations (300)
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
5311429 Tominaga May 1994 A
5387104 Corder Feb 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
5966685 Flanagan 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
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
6647364 Yumura et al. Nov 2003 B1
6691279 Yoden et al. Feb 2004 B2
6745161 Arnold et al. Jun 2004 B1
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
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
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
7174289 Sukehiro Feb 2007 B2
7177792 Knight et al. Feb 2007 B2
7191115 Moore 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
7356457 Pinkham et al. Apr 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
7409332 Moore 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
7565281 Appleby Jul 2009 B2
7574347 Wang Aug 2009 B2
7580830 Al-Onaizan et al. Aug 2009 B2
7620538 Marcu et al. Nov 2009 B2
7624005 Koehn et al. Nov 2009 B2
7624020 Yamada et al. Nov 2009 B2
7680646 Lux-Pogodalla et al. Mar 2010 B2
7689405 Marcu Mar 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
7974833 Soricut et al. Jul 2011 B2
8244519 Bicici et al. Aug 2012 B2
8415472 Chung et al. Apr 2013 B2
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
20020078091 Vu et al. Jun 2002 A1
20020099744 Coden et al. Jul 2002 A1
20020111788 Kimpara Aug 2002 A1
20020111789 Hull Aug 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
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
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
20040059708 Dean et al. Mar 2004 A1
20040068411 Scanlan Apr 2004 A1
20040098247 Moore May 2004 A1
20040111253 Luo et al. 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
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
20050060160 Roh et al. Mar 2005 A1
20050075858 Pournasseh et al. 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
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
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
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
20070050182 Sneddon et al. Mar 2007 A1
20070078654 Moore 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
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
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
20080270109 Och 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
20090326912 Ueffing Dec 2009 A1
20100017293 Lung et al. Jan 2010 A1
20100042398 Marcu et al. Feb 2010 A1
20100174524 Koehn Jul 2010 A1
20110029300 Marcu et al. Feb 2011 A1
20110082684 Soricut et al. Apr 2011 A1
Foreign Referenced Citations (7)
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
Non-Patent Literature Citations (219)
Entry
Nagao, Katashi, et al., “Semantic Annotation and Transcoding: Making Web Content More Accessible”, IEEE Multimedia, vol. 8, Issue 2Apr.-Jun. 2001, pp. 69-81.
Berhe, Girma, et al., “Modeling service-Based Multimedia Content Adaptation in Pervasive Computing”, CF '04, Ischia, Italy, Apr. 14-16, 2004, pp. 60-69.
Boitet, Christian, 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.
Llitjós, Ariadna Font, et al., “The Translation Correction Tool: English-Spanish User Studies”, Citeseer, © 2004, downloaded from: http://gs37.sp.cs.cmu.edu/ari/papers/Irec04/fontll, pp. 1-4.
Ballesteros, Lisa, et al., “Phrasal Translation and Query Expansion Techniques for Cross-Language Information”, SIGIR 97, Philadelphia, PA, © 1997, pp. 84-91.
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.
McDevitt, Kathleen, et al., “Designing of a Community-Based Translation Center”, Technical Report TR-03-30, Computer Science, Virginia Tech, © 2003, pp. 1-8.
Fleming, Michael, et al., “Mixed-Initiative Translation of Web Pages”, AMTA 2000, LNAI 1934, Springer-Verlag, Berlin, Germany, © 2000, pp. 25-29.
Perugini, Saverio, et al., “Enhancing Usability in CITIDEL: Multimodal, Multilingual and Interactive Visualization Interfaces”, JCDL '04, Tucson, AZ, Jun. 7-11, 2004, pp. 315-324.
Kishore Papineni et al, BLEU: a Method for Automatic Evaluation of Machine Translation, Jul. 2002, Google, 311-318.
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 Association for 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, San Diego, 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.
Bangalore, S. and Rambow, 0., “Evaluation Metrics for Generation,” 2000, Proc. of the 1st International Natural Language 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 18th conf. 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.
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.ac.il/ pUb/people/elhadad/fuf-life.lf).
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.
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 Syntatic Realization Component for Text Generation”, 1999 (available at http://www.cs.bug.ac.il/-elhadad/pub.html).
Franz Josef Och, Hermann Ney: “Improved Statistical Alignment Models” ACLOO: Proc. of the 38th Annual Meeting of the Association for Computational Linguistics, ‘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.
Fuji, Ren and Hongchi Shi, “Parallel Machine Translation: Principles and Practice,” Engineering of Complex Computer Systems, 2001 Proceedings, Seventh IEEE Int'l. Conference, pp. 249-259, 2001.
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 2004, July.
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 the Buck 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. (NPL0082).
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. 2003m, 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. of the14th 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, Philipp, “Noun Phrase Translation,” A PhD Dissertation for the University of Southern California, pp. xiii, 23, 25-57, 72-81, Dec. 2003.
Koehn, P. and Knight, K., “ChunkMT: Statistical Machine Translation with Richer Linguistic Knowledge,” Apr. 2002, Information Sciences Institution.
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.
Koehn, P. and Knight, K., “Knowledge Sources for Word-Level Translation Models,” 2001, Conference on Empirical Methods in Natural Language Processing.
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.
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.
Mann, G. and Yarowsky, D., “Multipath Translation Lexicon Induction via Bridge Languages,” 2001, Proc. of the 2nd 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.
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-Language Spoken Document Retrieval,” 2001, IEEE Workshop on Automatic Speech Recognition and Understanding. pp. 311-314.
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 Bias in Machine Learning,” 1996, Proc. of the Conference on Empirical Methods in Natural Language Processing, pp. 82-91.
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).
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.
Qun, Liu, “A Chinese-English Machine Translation System Based on Micro-Engine Architecture,” An Int'l. Conference on Translation and Information Technology, Hong Kong, Dec. 2000, pp. 1-10.
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.
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. of CoNLL-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.
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 the Americas on Machine Translation: From Research to Real Users, pp. 155-164.
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.
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 of the 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.
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).
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.
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>, Feb. 24, 2004.
Identifying, Dictionary.com, wayback.archive.org (Feb. 28, 2007) <http://wayback.archive.org/web/200501 01OOOOOO*/http:////dictionary.reference.com//browse//identifying>, Feb. 28, 2005 <http://web.archive.org/web/20070228150533/http://dictionary.reference.com/browse/identifying>.
Koehn, P. et al. “Statistical Phrase-Based Translation” Proceedings from 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>.
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>.
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 Linguisitcs, vol. 19, No. 1, pp. 75-102.
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.
Frederking et al., “Three Heads are Better Than One,” In Proceedings of the 4th Conference on Applied Natural Language Processing, Stuttgart, Germany, 1994, pp. 95-100.
Och et al., “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, 2002.
Yasuda et al., “Automatic Machine Translation Selection Scheme to Output the Best Result,” Proc of LREC, 2002, pp. 525-528.
Liu et al., “Context Discovery Using Attenuated Bloom Filters in Ad-Hoc Networks,” Springer, pp. 13-25, 2006.