Abstract:
Word sense ambiguity, for “thematic” words in a sentence, is achieved based on thematic prediction. The senses of “thematic” words are disambiguated in a sentence by determining and weighting possible themes for that sentence. Possible themes are determined for that sentence based on thematic information associated with the different senses of each word in the sentence. A highly deterministic thematic-based word sense disambiguation method is used to preprocess the sentence prior to further syntactic and semantic analysis, thereby enhancing accuracy and decreasing the demand for computational resources (memory and CPU) by reducing input ambiguities.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    1. Field of Invention  
           [0002]    The present invention relates generally to the field of natural language. More specifically, the present invention is related to word sense ambiguity reduction based on automatic theme prediction.  
           [0003]    2. Discussion of Prior Art  
           [0004]    Word sense disambiguation is the process of selecting the correct sense of each word in a sentence, based on the word&#39;s usage (or context) in the sentence. For example, the sense of the word “bank” as a noun in the English language is either “a building for keeping money safely” or “a land along the side of a river”, based on the context in which the word “bank” occurs. The accurate recognition of this distinction is particularly important in machine translation systems, because “bank” as a noun is translated differently depending on whether it meant the first sense or the second one.  
           [0005]    [0005]FIG. 1 illustrates the various natural language analysis systems. A natural language analysis system  100  is conventionally composed of two types of processes: processes which present possible alternatives (ambiguities)  102  to words; particularly nouns, in a sentence and processes which select correct alternatives (disambiguation)  104  to words based on the context of the sentence which is subject to analysis.  
           [0006]    [0006]FIG. 2 illustrates the various types of ambiguities associated with prior art natural language analysis systems. Ambiguities in natural language analysis come in three basic forms:  
           [0007]    Morphological ambiguity  202  occurs when a word has more than one part-of-speech. For example, the word “play” can be used as a verb or noun.  
           [0008]    Semantic ambiguity  204  occurs when a word/part-of-speech pair has more than one sense (meaning). For example, the word “bank” when used as a noun can have two different senses as described above.  
           [0009]    Syntactic (structural) ambiguity  206  occurs when a sentence (or a group of words) has more than one syntactic structure. For example, in the phrase, “a French book writer”, the term “French” may be an adjective modifying the word “book” or the word “writer”.  
           [0010]    [0010]FIG. 3 illustrates a prior art system  300  for natural language sentence analysis. The input to the system is a natural language sentence  302 , which is first segmented into separate word tokens using a tokenizer  304 . Each word token is then morphologically analyzed by a morphological analyzer (stemmer/lemmatizer)  306 , which in turn identifies all valid parts of speech for each input word, according to predefined stemming rules and based on lexicon  312  of the language (which contains for each stem all possible parts of speech). It should be noted that ‘stem’, as described in this patent application, is the basic form of any word token (e.g., the stem of “went” is “go”). The sentence, consisting of morphologically ambiguous part-of-speech tagged word tokens, then passes through a part-of-speech preliminary ambiguity resolver  308 , that disambiguates parts of speech in a quasi-deterministic fashion. Many conventional rule-based and statistical techniques are used to achieve this process. The part-of-speech tagged word tokens then pass through a lexicalizer  310 , which assigns each word/part-of-speech pair, one or more senses by accessing the language lexicon  312 . The sentence generated from lexicalizer  310 , which is now fully part-of-speech tagged and sense tagged is presented to syntactic &amp; semantic analyzer  314 , which resolves all embedded ambiguities in the input sentence by accessing a source with knowledge of grammar and word sense disambiguation and, as a result, generates a sentence with no ambiguities on morphological, semantic and syntactic levels.  
           [0011]    The main function of syntactic &amp; semantic analyzer  314  is to disambiguate the input sentence, that is, to select those correct possibilities out of the multitude of presented possibilities (ambiguities). Minimizing such ambiguities would further enhance the accuracy and performance of the disambiguation process. Hence, there is a need for a method and system that reduces the semantic ambiguity presented to the syntactic &amp; semantic analyzer. Whatever the precise merits, features and advantages of the above mentioned prior art systems, none of them achieve or fulfills the purposes of the present invention.  
         SUMMARY OF THE INVENTION  
         [0012]    The present invention provides for a method and system to provide accurate partial word sense disambiguation for “thematic” words in a sentence, based on thematic prediction. The method disambiguates the senses of “thematic” words in a sentence by determining and weighting possible themes for that sentence. The method determines possible themes for that sentence based on thematic information associated with the different senses of each word in the sentence. The present invention&#39;s highly deterministic thematic-based word sense disambiguation method preprocesses the sentence prior to further syntactic and semantic analysis. Thus, enhancing accuracy of the latter and decreasing its demand for computational resources (memory and CPU) by reducing input ambiguities. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]    [0013]FIG. 1 illustrates basic components of natural language analysis systems.  
         [0014]    [0014]FIG. 2 illustrates various types of ambiguities associated with prior art natural language analysis systems.  
         [0015]    [0015]FIG. 3 is a schematic representation of a natural language sentence analysis system as is known in the art.  
         [0016]    [0016]FIG. 4 is a flow chart of one embodiment of the steps in accordance with the present invention which performs word sense ambiguity reduction based on thematic prediction.  
         [0017]    [0017]FIG. 5 is a schematic representation of one embodiment of a software program in accordance with the present invention which reduces word sense ambiguities in a sentence based on thematic prediction.  
         [0018]    [0018]FIG. 6 is a schematic representation of one embodiment of the steps in accordance with the thematic predictor of the present invention.  
         [0019]    [0019]FIG. 7 is a schematic representation of one embodiment of the construction of the World Knowledge database in accordance with the present invention.  
         [0020]    [0020]FIG. 8 is a schematic representation of one embodiment of the construction of the theme-annotated lexicon in accordance with the present invention.  
         [0021]    [0021]FIG. 9 illustrates an example of sentence processed according to one embodiment of the method of the present invention.  
         [0022]    [0022]FIG. 10 illustrates an example of a sentence scored according to one embodiment of the present invention.  
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0023]    While this invention is illustrated and described in a preferred embodiment, the invention may be produced in many different configurations, forms and materials. There is depicted in the drawings, and will herein be described in detail, a preferred embodiment of the invention, with the understanding that the present disclosure is to be considered as an example of the principles of the invention and the associated functional specifications for its construction and is not intended to limit the invention to the embodiment illustrated. In particular, the present invention is useful in association with languages other than English or in addition to English. Those skilled in the art will envision many other possible variations within the scope of the present invention.  
         [0024]    The present invention provides a method for reducing sense ambiguity of part-of-speech tagged words in a sentence by automatic theme prediction based on the sentence local context. This sense ambiguity reduction is reached either through elimination of remotely probable senses or selection of highly probable senses. Thus, the method of the present invention results in:  
         [0025]    Increasing the overall accuracy of the analysis process  
         [0026]    Reducing the amount of required computational resources in terms of processing power and memory  
         [0027]    Speeding up the analysis process This highly deterministic and accurate theme-based word sense ambiguity reduction is useful in a variety of natural language processing applications, especially in natural language analysis systems.  
         [0028]    [0028]FIG. 4 illustrates method  400  of the present invention that provides, in response to a sentence input, theme-based reduction of sense ambiguity for the words of the input sentence. Method  400  starts by obtaining thematic information for each word in the input sentence (step  402 ). Next, a set of one or more probable themes for the input sentence (based on its context) is predicted (step  404 ). In step  406 , a computation is made to determine a score for each theme (based on the number of words carrying this theme) and part-of-speech tags assigned to these words). Lastly, in step  408 , sense ambiguity regarding each “thematic word” in the sentence is reduced either by eliminating a remotely probable sense or by selecting a highly probable sense. In this one embodiment of the present invention, elimination and/or selection of senses is performed only on thematic words, and is based on the dominant predicted theme. The dominant predicted theme is the predicted theme itself in case of only one predicted theme, and is the theme with the highest score in case of multiple predicted themes. It should be further noted that “thematic word”, as used in this patent application, is a word in the input sentence having at least one sense tagged with at least one theme.  
         [0029]    [0029]FIG. 5 is a representation of one embodiment of a software program in accordance with the present invention, which reduces word sense ambiguities in a sentence. The program of FIG. 5, as well as other programs described herein, may be implemented in various computing environments. For example, the present invention may be implemented on a conventional IBM PC or equivalent, multi-nodal system (e.g., LAN) or networking system (e.g., Internet, WWW, wireless web). All programming and data related thereto are stored in computer memory, static or dynamic, and may be retrieved by the user in any of: conventional computer storage, display (i.e., CRT) and/or hardcopy (i.e., printed) formats. The programming of the present invention may be implemented by one of skill in the art of natural language processing programming. Program  501  receives as input a sentence consisting of a series of part-of-speech tagged words, and produces as output the same sentence with reduced word sense ambiguities. Program  501  consists of thematic predictor  502 , thematic scorer  504 , and word sense disambiguator  506 .  
         [0030]    Thematic predictor  502  receives a sequence of part-of-speech tagged words and produces as output a sequence of sense tagged words and a set of one or more probable themes associated with the output sequence. Thematic scorer  504  receives as input the sequence of words generated from thematic predictor  502 , wherein each word is part-of-speech tagged, each part-of-speech is sense-tagged, and each sense is theme-tagged. In addition to the sequence of tagged words, a set of one or more predicted themes are presented to thematic scorer  504 . Thematic scorer  504  computes and assigns a score to each of the predicted themes and produces, as output, the same sequence of part-of-speech, sense, theme tagged words, and the same set of predicted themes, with each assigned a corresponding score.  
         [0031]    Word sense disambiguator  506  receives as input the same sequence of part-of-speech, sense, and theme tagged words, and the set of the weighted predicted themes generated from thematic scorer  504 , and outputs the same sequence of words with reduced sense ambiguities by eliminating remotely probable senses or selecting highly probable senses on thematic words. Part-of-speech tagged sentence words are the input sequence that is processed by thematic predictor  502 . A part-of-speech tagged word is built by stemming each word in the input sentence (by a stemmer) and annotating each word with the part-of-speech tags stored in a lexicon.  
         [0032]    [0032]FIG. 6 is representative of one embodiment of the present invention wherein a software program with thematic predictor  502  (FIG. 5) of the present invention automatically predicts, (from a predefined limited set of themes, as shown in Table 1 given below) one or more themes and assigns them to the input sentence. Thematic predictor  502  (FIG. 5) receives as input a sentence consisting of part-of-speech tagged words and produces, as output, the same sentence consisting of words tagged with part-of-speech, sense and theme, along with one or more automatically predicted theme assigned to the sentence as a whole. Prior to theme prediction, thematic predictor  502  (FIG. 5) searches a “World Knowledge” database  604 , to identify pre-stored names (step  602 ) in the database, in the input sentence, from the sentence start to its end, considering (N−1) succeeding words. N is defined as being the maximum number of words an entry in the “World Knowledge” database  604  has. It should be noted that, in one embodiment, the “World Knowledge” database  604  is accessible over a network such as, but not limited to, a wide area network (WAN), local area network (LAN), the Internet, or a wireless network. In the preferred embodiment of the present invention, the value of N is equal to 7. Whenever a “World Knowledge” entry is identified in the input sentence, the set of word tokens in the input sentence constituting this entry are catenated in one word and regarded as one word token throughout all further processing. The identified “World Knowledge” entry in the input sentence is tagged as “World Knowledge” token and the word token is further assigned one or more theme extracted from the corresponding “World Knowledge” database entry.  
                                         TABLE 1                                   Theme   No. of Referring Senses                                        Agriculture   2340           Education   776           Religion   1605           Transportation   1425           Medicine   3962           Economy   1980           Administration   805           Information   1794           Military   1564           Sports   1273           Entertainment   1519           Clerical   450           Politics   1696           Geography   2168           Ceremony   390           Law   1580           Arts   2411           Linguistics   1197           History   305           Science   649                      
 
         [0033]    At the end of this step, if at least one “World Knowledge” name is identified in the input sentence, the number of word tokens in this sentence is reduced by Y words which is computed as follows:  
             y   =       ∑     i   =   1       i   =   m            (       x   i     -   1     )               (     Equation                 1     )                               
 
         [0034]    Where m is the number of “World Knowledge” entries identified in the input sentence; x i  is the number of words in the i th  “World Knowledge” entry identified in the sentence.  
         [0035]    The above process is further clarified through the following example: Assume the simple sentence:  
         [0036]    “President Bill Clinton sent a veto to The United Nations.”  
         [0037]    After consultation of the “World Knowledge” database, two entries are identified: “Bill Clinton” and “The United Nations”, the individual tokens of each one of them will be catenated to constitute a single word token tagged with “World Knowledge” tag. A theme with a value=“Politics” is assigned to each one of both entries, according to thematic information extracted from the “World Knowledge” database for these specific entries. The new sentence becomes:  
         [0038]    “President BillClinton sent a veto to TheUnitedNations.”  
         [0039]    While the original input sentence length was 10 tokens, its length after searching the “World Knowledge” database has become 7 tokens.  
         [0040]    [0040]FIG. 7 illustrates, in further detail, the “World Knowledge” database  604  (FIG. 6) that consists of a limited predefined set of categories  702 , (humans, organizations, locations. . . . etc). Each category is further classified into a limited set of themes  704 , (politics, sports, military. . . etc). Each category-theme pair contains a limited set of entries  706 . This set of entries is always subject to expansion by feeding it continuously with new world knowledge. Each entry is composed of one or more tokens. Each token can be mandatory or optional: e.g. in the entry “The United Nations” the token “the” is optional while “United” and “Nations” are mandatory.  
         [0041]    Returning to the method in FIG. 6, the next step (step  606 ) in the thematic prediction phase involves collecting thematic information (via a thematic tag collector) from the lexicon  608  and identifying thematic words within the input sentence. The input to the thematic tags collector is a sentence consisting of a set of word tokens part-of-speech tagged. Some of these tokens, which have been identified in the “World Knowledge” database, are tagged as “world knowledge” tokens and are also tagged with one or more themes. The thematic tags collector produces as output the same input sentence consisting of word tokens, some of which are “world knowledge” tagged and theme-tagged, and others which are part-of-speech tagged, sense-tagged, and theme-tagged, i.e., the thematic tags collector assigns senses to those non-“world knowledge”-tagged tokens, and for each sense, one or more theme, if available.  
         [0042]    Sense and theme tagging is achieved by processing each word token of the sentence from the sentence start to its end. For each part-of speech of each non-“World Knowledge”-tagged word token, the lexicon is accessed and searched to match an identical word/part-of-speech pair. When found, all corresponding senses and themes are collected and assigned to the part-of-speech of the word token subject to processing. At the end of this stage, the input sentence consists of two types of word tokens: words tagged as “World Knowledge” tokens which are sense-unambiguous by definition and which are theme tagged and words that are part-of-speech tagged. Each word/part-of-speech pair is tagged with one or more sense, and each sense is tagged with zero, one or more theme. A word token carrying more than one sense is considered sense ambiguous, and a word token carrying at least one theme tag is considered a “thematic word”.  
         [0043]    [0043]FIG. 8 is a schematic representation of one embodiment of the construction of the theme-annotated lexicon in accordance with the present invention. The theme-annotated lexicon contains records  802  for each word. Each word is assigned one or more part-of-speech that may be associated with it. Each word/part-of-speech pair  804 , is assigned a set of senses  806 , each one of which, consisting of a sense description  808 , and a set of lexical and lexico-syntactic features. Each sense may be tagged with one or more theme  810 , selected out of a predetermined set of themes such as shown in Table1.  
         [0044]    In the example, the word “court” has been determined to occur as noun (tag “N”) and as verb (tag “V”). The word “court” occurs as noun with 4 possible different senses and occurs as verb with 4 other senses. The first sense of the noun part-of-speech for the word “court” has been determined as a thematic sense and has been assigned a theme tag with the value “Law”. Similarly senses  2 ,  3 ,  4  have been also determined as thematic senses and assigned respectively thematic tags with values: sports, and politics. For the verbal occurrence of the word “court”, none of its senses have been determined thematic; hence all of its 4 senses have been left out with no theme values.  
         [0045]    Returning to the discussion pertaining to FIG. 6, the last step in the phase of thematic prediction is the identification of the sentence themes  610 , which takes as input a sentence consisting of a stream of word tokens, some of which are tagged as “World Knowledge” tokens with theme tags, and the others are tagged with part-of-speech, sense and theme. The sentence themes identifier produces as output a set of one or more theme for the input sentence, by collecting all thematic values assigned to each sense of each part-of-speech for each part-of-speech tagged token, and all thematic values assigned to each “World Knowledge”-tagged token. After collecting all the themes, a union of theme values is generated consisting of a set of unique theme values; each one has associated with it an array of references to the source word tokens which generated it.  
         [0046]    [0046]FIGS. 9 and 10 collectively illustrate an example of a sentence processed by the system of the present invention. In the example shown in FIG. 9, for the input sentence  902 , “Andre Agassi is playing in the court”, “Andre Agassi” has been detected as a “World Knowledge” token and has been assigned a thematic value =“Sports”, hence becoming a thematic word token. In the same sentence, two other word tokens have been identified as thematic words, the word playing which occurs with only one part-of-speech tag “GRND” (gerund), and which has been assigned four different senses after consulting the lexicon, each of which was further tagged with thematic values, respectively: sports, entertainment, arts, and arts.  
         [0047]    The last thematic word in the input sentence is the word “court” which occurs with two different parts of speech: noun and verb. Each one assigned four different senses. The senses associated with the noun part-of-speech of “court” have been tagged with thematic values, respectively: law, sports, politics and politics, while none of the senses of the verb part-of-speech of “court” was assigned a theme value. As illustrated in FIG. 10, for sentence  902  of the current example, the sentence theme collector generates a set of unique themes  904 , namely: sports, politics, law, entertainment, arts. Associated with each one of the unique themes is array  906  of word token references, wherein each token reference  910  contains information about the word which generated the associated theme. Each reference can either be a reference for a “World Knowledge” token  912 , by specifying the token string, the token location in the sentence, or a part-of-speech tagged token  912 , by specifying the token string, and the token location in the sentence, the part-of-speech which contained the said theme and the sense number which contained the said theme.  
         [0048]    Thus, thematic scorer  504  (FIG. 5) takes as input the set of unique themes predicted for the input sentence by thematic predictor  502  (FIG. 5). Each of which has associated with it an array of word token references, as described previously. Thematic scorer  504  (FIG. 5) computes for each of the predicted themes a score, assigns the computed score to the said theme, then ranks predicted unique themes based on the score value associated with each one. The higher the score associated with a specific theme, the higher the probability that the input sentence belongs to this specific theme.  
         [0049]    Theme score is calculated according to the following equation:  
               T                   S   k       =       ∑     i   =   1       i   =   n            c   i               (     Equation                 2     )                               
 
         [0050]    Where TS k  is the score value for theme k; n is the number of references for theme k, considering only one reference per word token/part-of-speech pair, and c i  is the coefficient of the i th  reference element for theme k. The coefficient for the referencing element i depends on the type (World Knowledge/Non-World Knowledge) and the part-of-speech value, which is one of the four basic parts of speech: noun, verb, adjective, adverb.  
         [0051]    Table 2 (given below) shows the different values coefficient c i  can take based on the type/part-of-speech of the associated element i. The reduction of any part-of-speech to its basic part-of-speech is achieved through a predefined part-of-speech mapping table, Table 3 (also given below), consisting of a limited set of records, each one composed of two fields. The first one is the part-of-speech that needs to be mapped and the second one is the basic part-of-speech to which the original one maps.  
                           TABLE 2                                   Type/part-of-speech   c (value)                           World Knowledge   5           Noun   4           Verb   3           Adjective   2           Adverb   1                      
 
         [0052]    [0052]                           TABLE 3                                   Original part-of-speech   Basic part-of-speech                           Past participle   Verb           Present 3 rd  person   Verb           Gerund   Verb           Comparative adjective   Adj.           Superlative adjective   Adj.           Plural noun   Noun           Proper noun   Noun           Mass noun   Noun                        
         [0053]    According to the theme scoring process described above, the scores  908  for the predicted themes of the example sentence  902  are ranked resulting in the predominant theme: “Sports”. After prediction and scoring of the themes in the input sentence, comes the step  408  (FIG. 4) of reducing word-sense ambiguity on thematic words of the said input sentence.  
         [0054]    The input to this phase is the input sentence consisting of word tokens, some of which have been tagged as World Knowledge tokens, and the others are part-of-speech, sense, and theme tagged. The second input to this phase is the scored predicted themes for the said input sentence. The function of this phase is to reduce sense-ambiguity of sense-ambiguous thematic words for the input sentence. This sense ambiguity reduction is achieved by either selecting highly probable senses or eliminating remotely probable senses on sense-ambiguous thematic words.  
         [0055]    A sense ambiguous word is one that has at least one part-of-speech having more than one sense. A World Knowledge-tagged word token is always considered sense unambiguous. A thematic word token is one that contains at least one theme on the senses associated with it. Based on the above description, the example sentence shown in FIG. 7 , would have  3  thematic word tokens: “Andrea Agassi”, “playing” and “court”; and would have  2  sense-ambiguous words which are: “playing” and “court”.  
         [0056]    Word sense reduction is initiated only if a dominant sentence theme is identified, and the number of thematic words is equal or higher than ¼ of the total number of word tokens in the input sentence, assuming “World Knowledge” tokens as single tokens. The dominant theme is determined as being the theme with the highest score among the predicted themes in case of plurality of predicted themes, and is assumed the predicted theme in case of one predicted theme only. In case there is more than one dominant theme, i.e., there are more than one theme possessing the same highest score, no dominant theme is determined and hence the reduction process is stopped.  
         [0057]    Sense ambiguity reduction is achieved as follows: For each part-of-speech of each thematic word token in the input sentence, if the part-of-speech has more than one sense, and at least one of the senses has a thematic value matching the dominant predicted theme, then sense reduction is performed by eliminating all the senses which do not match the predicted dominant theme. By applying the above mentioned method for sense ambiguity reduction, on the example sentence shown in FIGS.  9 , the dominant predicted theme is “Sports”, the second, third and fourth senses of word “playing” as “gerund”, are eliminated, and the first, third and fourth senses of word “court” as “noun” are eliminated. FIG. 10 illustrates scoring the themes associated with the sentence illustrated in FIG. 9.  
         [0058]    A system and method has been shown in the above embodiments for the effective implementation of a method and system for theme-based word sense ambiguity reduction. While various preferred embodiments have been shown and described, it will be understood that there is no intent to limit the invention by such disclosure, but rather, it is intended to cover all modifications and alternate constructions falling within the spirit and scope of the invention, as defined in the claims. In particular, the present invention should not be limited by software/program, computing environment or specific computing hardware.