Patent Application
20070010992
The discussion below is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.
With an ever increasing global economy, and with the rapid development of the Internet, people all over the world are becoming increasingly familiar with writing in a language which is not their native language. Unfortunately, for some societies that possess significantly different cultures and writing styles, the ability to write in some non-native languages is an ever-present barrier. When writing in a non-native language (for example English), language usage mistakes are frequently made by non-native speakers (for example, people who speak Chinese, Japanese, Korean or other non-English languages). These kind of mistakes can include both grammatical mistakes and improper usage of collocations such as verb-object, adjective-noun, adverb-verb, etc.
Many people have the ability to write in a non-native language using proper grammar, but they still may struggle with mistakes in collocations between two words. Still others struggle with both grammar and other mistakes such as collocations between two words. While spell checking and grammar checking programs are useful in correcting grammatical mistakes, detection and/or correction of mistakes in collocations between two words can be difficult, particularly since these mistakes can be otherwise grammatically correct. Therefore, grammar checkers typically provide very little assistance, if any, in detecting mistakes relating to the collocation between words. English is used as an example of the non-native language in the following discussion, but these problems persist across other language boundaries.
For example, consider the following sentences that contain collocation mistakes which cause the sentences to not be native-like English, even if otherwise grammatically correct.
The native-like English versions of these sentences should be like:
As an example of the barriers faced by non-native English speaking people, consider the plight of Chinese users. By culture, background and thinking habits, Chinese people often produce English sentences which may be grammatical, but not natural. For example, Chinese people tend to directly translate subjects in Chinese into subjects in English, and do the same with objects and verbs. When writing in English, Chinese people often experience difficulty in deciding the collocations between verbs and prepositions, adjectives and nouns, verbs and nouns, etc. Moreover, in specific domains like the business domain, special writing skills and styles are needed.
Common dictionaries are mainly used by non-native speakers for the purpose of reading (a kind of decoding process), but these dictionaries do not provide enough support for writing (a kind of encoding process). They only provide the explanation of a single word, and they typically do not provide sufficient information to explain relevant phrases and collocations. Moreover, there is no easy way to get this kind of information from dictionaries, even if some of the information is provided in the dictionaries. On the other hand, current widely used grammar checking tools have some limited ability in detecting apt-to-make grammatical mistakes, but are not able to detect the collocation mistakes.
This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
A sentence is accessed and at least one query is generated based on the sentence. At least one query can be compared to text within a collection of documents, for example using a web search engine. Collocation errors in the sentence can be detected and/or corrected based on the comparison of the at least one query and the text within the collection of documents.
The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, telephony systems, distributed computing environments that include any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. Tasks performed by the programs and modules are described below and with the aid of figures. Those skilled in the art can implement the description and figures as processor executable instructions, which can be written on any form of a computer readable medium.
With reference to
Computer 110 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 110 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 110. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media.
The system memory 130 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 131 and random access memory (RAM) 132. A basic input/output system 133 (BIOS), containing the basic routines that help to transfer information between elements within computer 110, such as during start-up, is typically stored in ROM 131. RAM 132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 120. By way of example, and not limitation,
The computer 110 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,
The drives and their associated computer storage media discussed above and illustrated in
A user may enter commands and information into the computer 110 through input devices such as a keyboard 162, a microphone 163, and a pointing device 161, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 120 through a user input interface 160 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor 191 or other type of display device is also connected to the system bus 121 via an interface, such as a video interface 190. In addition to the monitor, computers may also include other peripheral output devices such as speakers 197 and printer 196, which may be connected through an output peripheral interface 190.
The computer 110 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 180. The remote computer 180 may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 110. The logical connections depicted in
When used in a LAN networking environment, the computer 110 is connected to the LAN 171 through a network interface or adapter 170. When used in a WAN networking environment, the computer 110 typically includes a modem 172 or other means for establishing communications over the WAN 173, such as the Internet. The modem 172, which may be internal or external, may be connected to the system bus 121 via the user input interface 160, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 110, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,
Preprocessing module 202 processes text to provide part of speech tagging and chunk parsing of the text. Many different types of parsers can be used to process the text. Below is an example sentence:
I have recognized this person for years.
Preprocessing module 202 tags this sentence and chunks the sentence as follows:
[NP I/PRP][VP have/VBP recognized/VBN][NP this/DT person/NN][PP for/IN][NP years. </s>/NNS]
Using the processed text, query generation module 204 constructs queries. In one example, four sets of queries are generated for each type of collocation error type identified above. For example, the collocation error types can be verb-noun, preposition-noun, adjective-noun and verb-adverb. The queries generated can include the full text of the sentence as well as a reduced portion of the sentence where auxiliaries are removed. Example reduced queries for the sentence above can include, “have recognized this person”, “have recognized”, “this person” and “recognized person”.
The queries are submitted to a search module 206. In one embodiment, the search module can be a web based search engine such as MSN Search (search.msn.com), Google (www.google.com) and/or Yahoo! (www.yahoo.com). Portions of text similar to the queries are retrieved from the search module based on results from the query submission. Since the web includes a vast amount of text, it can be an inexpensive resource to detect collocation errors. Error detection module 208 compares the queries generated by query generation module 204 to results obtained by search module 206. Error correction module 210 provides candidate corrections for the errors identified by error detection module 208.
Example queries for the sentence “I have recognized this person for years” for type VN detection are presented below, where˜means that two adjacent words can be adjacent or one-word away from each other.
S-Query: [“I have recognized this person for years”]
S-Query: [“have recognized this person”]
C-Query: [“have recognized”˜“this person”]
W-Query: [“recognized”˜“person”]
Example rules for generating queries of each type are as follows.
However, if a match does not exist, method 250 proceeds to step 258, wherein C-queries 244 are compared to the search module results. At step 260, it is determined whether one or more of the C-queries closely match the search module results and if a score for the comparison is greater than a threshold. In one example, the score is computed by dividing a number of times the chunk of the C-query appears in the search results by the number of times words in the C-query co-occur in the search results. If the score is greater than the threshold, it is determined that no collocation error exists at step 256.
If the score is less than the threshold, method 250 proceeds to step 262, wherein W-Queries are compared to the search engine data. Step 264 determines whether there is a close match between the W-queries and the search engine data and whether a score for the comparison is greater than a threshold. If the score is greater than the threshold, it is determined that no collocation error exists at step 256. The score for the comparison can be similar to the C-Query comparison score. Thus, the W-Query comparison score can be calculated by dividing the number of times the W-Query occurs in the search results by the total number of times the pair of words in the W-Query co-occurs. If the score is less than the threshold, method 250 proceeds to step 266, wherein the user is notified of a potential collocation error.
S-QT: [“I have + this person for years”]
S-QT: [“I have + this person”]
S-QT: [“have + this person for years”]
S-QT: [“I have + this person”]
C-QT: [“+ this person for years”]
C-QT: [“+ this person”]
Example rules for generating query templates can be as follows.
At step 274, the query template is submitted to a search module, herein a search engine. At step 276, strings from the search engine results are retrieved. The strings can include summaries of text that have words of surrounding context. Strings that match the query templates, where the position of + can be any one word, are identified as strings candidates. Candidates that do not contain the collocation (which is formed by a word replacing + and another word in the string according to the collocation type) are removed at step 278. Remaining candidates are ranked according to a score based on a corresponding weight of query template that matched the string candidate. For example, the weight of the query template can be based on the number of words in the query template. The score for each candidate is calculated by taking a sum for weights across all summaries containing the candidates. The score for query templates (QTs) that retrieve the candidate can be expressed by:
Score(candidate)=ΣQTs Weight(QT)
A ranked list of candidates is then presented to a user at step 280. For example, a pop-up menu can be used to present the ranked list. A user can choose one of the selections from the list to correct the collocation error.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
