Abstract:
A system for automatically generating a dictionary from full text articles extracts &lt;term, definition&gt; pairs from full text articles and stores the &lt;term, definition&gt; pairs as dictionary entries. The system includes a computer readable corpus having a plurality of documents therein. A pattern processing module ( 120 ) and a grammar processing module ( 125 ) are provided for extracting &lt;term, definition&gt; pairs from the corpus and storing the &lt;term, definition&gt; pairs in a dictionary database ( 145 ). A routing processing module selectively routes sentences in the corpus to at least one of the pattern processing module or grammar processing module. In one embodiment, the routing module is incorporated into the pattern processing module which then selectively routes a portion of the sentences to the grammar processing module. A bootstrapping processing module ( 150 ) can be used to apply &lt;term, definition&gt; entries against the corpus to identify and extract additional &lt;terms, definition&gt; entries.

Description:
RELATED APPLICATIONS 
   The present application claims the benefit of U.S. Provisional Application Ser. No. 60/324,880, entitled “Method for Identifying Definitions and their Technical Terms from on-line Text for Automatically Building a Glossary of Terms,” filed on Sep. 27, 2001, the disclosure of which is hereby incorporated by reference in its entirety. 

   FIELD OF THE INVENTION 
   The present invention relates generally to language processing and more particularly relates to a method for automatically generating dictionary entries using text analysis. 
   BACKGROUND OF THE INVENTION 
   The internet has enjoyed tremendous growth over recent years. As a result, vast quantities of information are readily available to millions of users. Among the vast content available on the internet are technical papers, which are of interest to a large number of people, but may be written for a technical audience and assume a baseline understanding of terms used in the particular field. Since now, more than ever, this assumption is not necessarily true, the importance of on-line dictionaries of technical terms is of growing importance. 
   On-line dictionaries of technical terms have been difficult to build and are often lacking in completeness. For example, technical dictionaries such as the Online Medical Dictionary (OMD, http://www.graylab.ac.uk/omd/index.html), are often missing common terms which are assumed to be understood by those practicing in the particular field. In addition, the definitions in such dictionaries are often too technical for use by a lay person. Accordingly, it would be desirable to automatically generate on-line glossaries of technical terms that are comprehensive and generally useful to the technically oriented user as well as the lay person. 
   SUMMARY OF THE INVENTION 
   It is an object of the present invention to provide a system for automatically generating dictionaries based on an analysis of full text articles. 
   It is an object of the present invention to provide a system for automatically generating dictionaries for various technical domains based on an analysis of fall text articles. 
   It is a further object of the present invention to provide a system and method for extracting term-definition pairs from full text articles, the term-definition pairs being capable of use as dictionary entries. 
   In accordance with the invention, a computer-based method for automatically generating a dictionary based on a corpus of fall text articles is provided. The method applies linguistic pattern analysis to the sentences in the corpus to extract simple &lt;term, definition&gt; pairs and identify sentences with candidate complex &lt;term, definition&gt; pairs. Grammar analysis is then applied to the sentences with candidate complex &lt;term, definition&gt; pairs to extract &lt;term, definition&gt; pairs. The extracted &lt;term, definition&gt; pairs are then stored in a dictionary database. 
   Pattern analysis can include identifying sentences having text markers and predetermined cue phrases and subjecting the identified sentences to rule based &lt;term, definition&gt; extraction. Sentences which include text markers can be further processed by a filtering operation to remove sentences which are not indicative of having &lt;term, definition&gt; pairs. Grammar processing generally operates upon sentences which include apposition or are in the form term is definition. 
   Also in accordance with the present invention is a system for automatically generating a dictionary from full text articles. The system includes a computer readable corpus having a plurality of documents therein. A pattern processing module and a grammar processing module for extracting &lt;term, definition&gt; pairs from the corpus and storing the &lt;term, definition&gt; pairs in a dictionary database are also provided. A routing processing module is provided to selectively route sentences in the corpus to at least one of the pattern processing module and grammar processing module. 
   Preferably, the system further includes a bootstrap processing module. The bootstrap processing module applies entries in the dictionary database to the corpus and extracts and stores additional &lt;term, definition&gt; pairs in the dictionary database. 
   In one embodiment, the routing processing module tags sentences which may include &lt;term, definition&gt; pairs and routes all tagged sentences to the pattern processing module. In addition to extracting certain &lt;term, definition&gt; pairs, the pattern processing module performs the additional operation of identifying sentences which include candidate complex &lt;term, definition&gt; pairs. The grammar processing module then receives the sentences having candidate complex &lt;term, definition&gt; pairs from the pattern processing module and operates to extract &lt;term, definition&gt; pairs from these sentences. 

   
     BRIEF DESCRIPTION OF THE DRAWING 
     Further objects, features and advantages of the invention will become apparent from the following detailed description taken in conjunction with the accompanying figures showing illustrative embodiments of the invention, in which: 
       FIG. 1  is a flow diagram illustrating the overall operation of a system for generating entries for a dictionary by extracting term-definition pairs from full text articles; 
       FIG. 2  is a flow chart further illustrating the operation of an add linguistic markup block of  FIG. 1 ; 
       FIGS. 3A and 3B  are a flow chart illustrating the pattern analysis processing operation of  FIG. 1  in greater detail; 
       FIG. 4  is a is a flow chart illustrating the grammar analysis processing operation of  FIG. 1  in greater detail; 
       FIG. 5A  is a pictorial representation of an example of a parse tree output from a grammar parsing program, such as English Slot Grammar; 
       FIG. 5B  is a pictorial representation of an example of a parse tree output from a statistical grammar parsing program, such as Charniak&#39;s statistical parser; 
       FIG. 6  is a simplified flow chart illustrating an exemplary apposition processing subroutine suitable for use in connection with grammar analysis processing; 
       FIG. 7  is a simplified flow chart illustrating a subroutine for extracting &lt;term, definition&gt; tuples from sentences in the form TERM is DEFINITION, which is suitable for use in connection with grammar analysis processing; 
       FIG. 8  is a simplified flow chart further illustrating the steps performed by a bootstrapping algorithm, which is suitable for use in expanding the rule based dictionary database; 
       FIG. 9  is a flow diagram illustrating the overall operation of an alternate embodiment of a system for generating entries for a dictionary by extracting &lt;term, definition&gt; pairs from a corpus of full text articles; 
       FIG. 10  is a flow chart further illustrating the operation of a module selection logic block of  FIG. 9 ; 
       FIG. 11  is a flow chart illustrating the pattern analysis processing operation of  FIG. 9  in greater detail; 
   

   Throughout the figures, the same reference numerals and characters, unless otherwise stated, are used to denote like features, elements, components or portions of the illustrated embodiments. Moreover, while the subject invention will now be described in detail with reference to the figures, it is done so in connection with the illustrative embodiments. It is intended that changes and modifications can be made to the described embodiments without departing from the true scope and spirit of the subject invention as defined by the appended claims. 
   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     FIG. 1  is a flow diagram illustrating the overall operation of a first embodiment of a system for generating entries for a dictionary by extracting &lt;term, definition&gt; pairs or tuples, from full text articles. While the system and methods of  FIG. 1  will be described using the example of the medical field, the present invention can be applied to any body of text from which a user wishes to build a computer readable dictionary. The method of  FIG. 1  is generally performed on a conventional computer system, which is operatively coupled to a source of full text articles. The articles can be stored in a local database or can be accessed over a digital communications network, such as the internet. 
   In step  105 , the articles, in computer readable form, such as ASCII, HTML and the like, are input to the system. The articles are passed to a preprocessing algorithm, which tokenizes the input articles and formats the articles in a manner which is suitable for processing by text parser and part-of-speech tagging algorithms. The preprocessing operations of step  110  can include stripping away HTML tags, tokenizing the text and rewriting the text file as one sentence per line with each line numbered and identified to its source text. Tokenizing the text generally includes separating, by a space, each unit of a sentence (word, punctuation, number) that can be considered an independent token. Generally, hyphenated words are maintained as a single token. 
   Following preprocessing, an Add Linguistic Markup operation is performed which is used to identify sentences which may include &lt;term, definition&gt; tuples (step  115 ). The operation of the Add Linguistic Markup operation of step  115  is further illustrated in  FIG. 2 . 
   Referring to  FIG. 2 , adding linguistic mark ups begins by applying part-of-speech (POS) tagging to assign a part of speech label to each word (step  210 ). A number of known POS tagging programs can be used in connection with step  210 . One suitable POS tagging program is described by E. Brill, “A Simple Rule-based Part of Speech Tagger,” Proceedings of the Third Conference on Applied Natural Language Processing, Trento, Italy, 1992, the disclosure of which is hereby incorporated by reference in its entirety. 
   Following POS tagging, a noun phrase identification operation, referred to in the art as Noun Phrase Chunking, is applied in step  215  to identify various forms of noun phrases in the sentences being processed. A suitable method for performing Noun Phrase Chunking is described by Ramshaw and Marcus in “Text Chunking Using Transformation-Based Learning,” Proceedings of Third ACL Workshop on Very Large Corpora,” MIT, 1995, the disclosure of which is hereby incorporated by reference in its entirety. The Noun Phrase Chunking algorithm identifies noun phrases in the sentences being analyzed and sets the noun phrases apart from the other text, such as by insertion of brackets. For example, the sentence “Arrhythmia—irregular heartbeat—is experienced by millions of people in a variety of forms.” will be tagged as follows:
     [Arrhythmia/NNP]--/: [irregular/JJ heartbeat/NN]--/: is/VBZ experienced/VBN by/IN [millions/NNS] of/IN [people/NNS] in/IN [a/DT variety/NN] of/IN forms/NNS;
 
where NNP represents a proper noun, JJ represents an adjective, NN represents common noun, VBZ is verb, present tense, singular, VBN is verb, past participles, IN means preposition, DT refers to determiner and NNS refers to common noun, plural.
   

   After Noun Phrase Chunking, the input sentences are evaluated to determine whether they include either text markers or cue phrases (Step  220 ). Text markers that have been found to be indicative of a definition include hyphens, such as --, and parenthetical expressions following noun phrases. Cue phrases which are indicative of a definition include forms of “X is the term used to describe Y,” as opposed to phrases such as “for example” or “such as” which tend to indicate explanation rather than definition. Those sentences which are found to include cue phrases and/or text markers in step  220  are marked with tags in step  225 . In step  240 , the sentences marked in step  225  are routed to a pattern analysis module  120  which performs shallow parsing of the sentence to extract simple &lt;term, definition&gt; tuples and also identifies candidate sentences which may include complex definitions. 
   If in step  220  the sentence being evaluated does not include text markers, the sentence is further evaluated to determine whether the sentence includes possible anaphora or represents a form of term is definition (step  230 ). Such sentences are marked with tags indicating the possible &lt;term, definition&gt; tuple in step  235  and the marked sentences are routed to the pattern analysis module (Step  240 ). If the test performed in step  230  fails, the sentence is rejected as not including a &lt;term, definition&gt; tuple. 
   The sentences that are marked in steps  225  and  235  are passed to the pattern analysis module which is further described in connection with  FIGS. 3A and 3B . In step  310 , the sentences are evaluated for the presence of text markers. Those sentences which include text markers are then subjected to a number of filtering rules in step  315  ( FIG. 3B ) which are designed to eliminate sentences which are not likely to possess &lt;term, definition&gt; tuples. The filtering rules generally will remove sentences which have conjunctions at the beginning of a text marker. In addition, the filtering rules will identify and remove phrases that indicate explanation rather than definition, such as “for example,” “for instance” and the like. The filtering rules can also identify and eliminate sentences having lists of commas and conjunctions, which indicate enumeration and have not been found to identify &lt;term, definition&gt; pairs. It will be appreciated that additional rules may be found to be useful in identifying and eliminating phrases set off by text markers which are not indicative of term-definition pairs and that such rules could also be implemented in step  315 . 
   Following the filtering operations of step  315 , the remaining sentences including text markers are analyzed to identify simple noun phrase patterns in the form: Noun Phrase  1  {text marker} Noun Phrase  2  (step  320 ). For those sentences which include such noun phrase patterns, which represent a &lt;term, definition&gt; pair, the term and definition need to be identified in step  325 . One method of identifying the term and definition components of the &lt;term, definition&gt; tuple is to determine the frequency of occurrence of Noun Phrase  1  and Noun Phrase  2 . The noun phrase having the higher frequency of occurrence is designated the term and the other noun phrase is considered the definition for the term (step  325 ). The &lt;term, definition&gt; tuples can be used to form a hash array where the terms are keys to the array and the definitions are the values of the array. The hash array is then added to the pattern dictionary database  130 . 
   If in step  320 , the sentence does not represent a simple noun phrase pattern, the sentence is further evaluated to determine whether the sentence may include a complex definition and be a candidate for further grammar processing (step  330 ). Sentences of various forms having syntactic structures more complex than Noun Phrase  1  {text marker} Noun Phrase  2  can be candidates for complex definitions. For example sentences having a form Noun Phrase  1  {text marker} Noun Phrase  2  (.*), where (.*) represents any additional text, can be identified a candidates which may include complex definitions. Sentences which include possible complex definitions are stored in a hash array for subsequent grammar processing which will be described below in connection with  FIG. 4  (step  335 ). If in step  330 , the sentences do not include candidates for complex definitions, the sentences are removed from further processing. 
   Returning to  FIG. 3A , in step  310 , those sentences which do not include text markers are passed to step  340  which evaluates the input sentences to identify cue phrases within the sentences. A non-exhaustive list of cue phrases includes: “is the term used to describe”, “is defined as”, “is called” and the like. Sentences including cue phrases are parsed by identifying the context on the left hand side (LHS) of the cue phrase and the context on the right hand side (RHS) of the cue phrase (step  345 ). If the left hand side is a noun phrase, then the LHS noun phrase will be considered a term and the right hand side will be considered a definition for the term (step  350 ). The &lt;term, definition&gt; pair can be used to generate a hash array and added to the pattern dictionary database  130   
   Those sentences which do not include cue phrases in step  340  are further evaluated to determine if they include simple patterns which have been found to be representative of simple &lt;term, definition&gt; tuples (step  355 ). Such simple patterns include {Noun Phrase is Noun Phrase} and {Noun Phrase, or (optional) Noun Phrase, |}. For example, “myocardial infarction, heart attack . . . ” and “myocardial infarction, or heart attack . . . ” illustrate such patterns. &lt;Term, definition&gt; tuples are extracted from sentences having the simple patterns tested for in step  355  and the extracted &lt;term, definition&gt; tuples are added to the pattern dictionary  130 . 
   Those sentences in step  355  which do not include simple patterns are further analyzed to determine if the sentences may include complex definitions (step  360 ). As in the case of sentences having text markers, sentences of various forms having syntactic structures more complex than Noun Phrase  1  is Noun Phrase  2  can be candidates for complex definitions. If a sentence is identified as including a candidate complex definition, the sentence is added to a hash table and is stored for additional processing (step  370 ). Otherwise, the sentence is discarded from additional processing. 
   Returning to  FIG. 1 , the sentences that are placed in the hash array as including candidates for complex definitions are passed from the pattern analysis processing block  120  to the grammar analysis processing block  125  to determine if this subset of sentences include &lt;term, definition&gt; tuples. The operation of the grammar analysis block  125  is further illustrated in the flow diagram of  FIG. 4 . 
   Referring to  FIG. 4 , grammar analysis processing begins with a grammar parsing operation (step  405 ). Grammar parsing can be performed with known parsing programs such as the English Slot Grammar (ESG) parser program, available from International Business Machines and described in “The Slot Grammar System,” by McCord, IBM Research Report, 1991, or a statistical parser, such as that described by E. Charniak in “A Maximum Entropy Inspired Parser,” Proceedings of NAACL 2002, the disclosure of which is hereby incorporated by reference. These parser programs are used to parse each input file and obtain a parsed form for each sentence (step  405 ). For each sentence, the ASCII-style output which is generally provided by the parser program is formatted as a parsed data structure that provides the dependency between the words in the sentences, the hash array that contains the feature structures associated with each word, which are the nodes in the tree, and the slot filler type of each node (step  410 ). 
   A typical parsed tree structure based on the ESG parser is illustrated, for example, in  FIG. 5A . In  FIG. 5A , the first column  505  indicates the slot filled by the node (word), the second column  510  lists the words and positions of the words, the third column  520  lists the feature structure for each word.  FIG. 5B  illustrates an example of parsed output using Charniak&#39;s statistical parser. 
   Returning to  FIG. 4 , the parsed tree is traversed to determine the daughters and ancestors of each node (step  415 ). The sentences are then evaluated to determine if they include apposition (step  420 ) and if they do, processing is passed to apposition processing logic in step  425 . If in step  420  the sentences do not include apposition, the sentences are evaluated to determine if the sentences are of the form term is definition (&lt;T is D&gt;). For sentences in the form of &lt;T is D&gt;, &lt;T is D&gt; processing block  435  is called to identify and extract term-definition pairs. 
     FIG. 6  is a flow chart illustrating an example of logic which can be used to process sentences including apposition in order to extract &lt;term, definition&gt; pairs. In step  605  the tree structure is evaluated to determine whether the apposition word (‘,’ ‘--’ ‘(’, ) has ancestors. If so, then the left conjunction (lconj) is identified and evaluated to determine if the lconj is a noun (step  610 ). Similarly, the right conjunction (rconj) is evaluated to determine if it is a noun (step  615 ). The right conjunction is also tested to determine whether it includes an additional comma or conjunction, which would be indicative of enumeration rather than definition (step  620 ). If the lconj=noun, rconj=noun and rconj does not indicate enumeration, the subtree rooted at lconj is labeled as the term, the subtree rooted at rconj is labeled as the definition and the term-definition pair is added to the hash tree (step  625 ) as an entry for the grammar dictionary ( 135 ). If any of the decision blocks  605 ,  610 ,  615  or  620  fail, the sentence is considered as not including a &lt;term, definition&gt; tuple (block  630 ). 
     FIG. 7  is a flow chart illustrating an example of logic which can be used to process sentences in the form term is definition, &lt;T is D&gt;, to extract &lt;term, definition&gt; pairs. In step  705 , the parsed tree of the sentence is evaluated to determine if the root of the tree is a present tense form of the verb “to be.” If so, then in step  710  the subject of the sentence is evaluated to determine if the subject is a noun. If the subject is noun, then flow proceeds to step  715  where the daughters of the subject are evaluated to determine whether the daughters are left conjunctions (lconj) and right conjunctions (rconj). If in step  715  the daughters are not lconj, rconj, then the predicate in the sentence is tested in step  720  to determine if the predicate is a noun. If the predicate is a noun, then the subject is identified as the term and the subtree rooted at the predicate is identified as the definition (step  725 ). The &lt;term, definition&gt; pair is then added to the hash table in the grammar dictionary  135 . 
   Returning to step  715 , if the daughters of the subject are in the form lconj, rconj, than the lconj and rconj terms are evaluated to determine if they are nouns, which indicates that these daughters are possible synonyms of the subject. If the lconj and rconj are nouns, then each of these daughters is labeled as additional terms (step  745 ). The predicate of the sentence is then tested to determine if it is a noun in step  750 . If the predicate is a noun, than the subtree rooted at the predicate is labeled as the definition for each of the terms. The &lt;term, definition&gt; pairs are then added to the hash array which are then entered into the grammar dictionary  135 . If any of the conditional tests of steps  705 ,  710 ,  720 ,  740 , or  750  fail, then the sentence is considered as not including a term-definition pair. 
   Referring to  FIG. 1 , the merged dictionary database  145  can be further expanded with additional &lt;term, definition&gt; pairs by applying a bootstrapping algorithm in step  150 , such as is described in further detail in  FIG. 8 . 
   Referring to  FIG. 8 , the &lt;term, definition&gt; pairs of the merged dictionary database  145  are used as seed tuples in the initialization step (step  805 ). Then, the terms are identified in the corpora of full text articles (step  810 ) and the left hand side context and right hand side context of each term is identified inside the sentence where the term is identified (step  815 ). A matching algorithm is then used to identify correspondence between the definition and the left hand side and right hand side context, respectively (step  820 ). To increase flexibility, word order is not considered by the algorithm. When a definition is subsumed by either the left side or ride side context or overlaps either context sufficiently, the context is considered as a match to the definition and the exact match is called Def. 
   After matching is performed a new tuple of the form &lt;l,T,m,Def,r&gt; is recorded that keeps the left(l), the middle(m) and the right(r) context inside the sentence (step  825 ). These contexts are in connection with both term and definition. An example of middle context is: (&lt;term&gt; is characterized by &lt;definition&gt;). It has been observed that the middle context can be more important than right or left contexts. Thus it may be preferable to assigned more weight to m. 
   The sentence from the corpus that contains the &lt;term, definition&gt; pair is parsed using a statistical parser, such as that described by E. Charniak in “A Maximum Entropy Inspired Parser,” Proceedings of NAACL 2002, the disclosure of which is hereby incorporated by reference. The statistical parser can be used to generate candidate patterns for identifying additional &lt;term, definition&gt; tuples in the corpus in the following iterative steps. 
   Candidate patterns are identified from the parse tree (step  830 ) by performing a matching algorithm on the parse tree of the sentences from the corpus and the initial tuple &lt;term, definition&gt; parse tree. The subtrees are matched to a corresponding T and Def and the tree pattern covering these two subtrees is recorded. 
   A best rank score can then be used to select new patterns which identify &lt;term, definition&gt; tuples in the corpora. The rank score is similar to a RlogF measure used in AutoSlog-TS, by E. Riloff in the paper. “Automatically generating Extraction Patterns from Untagged Text”, Proceeding of AAAI 1996. The rank score is computed as: score(patern)=,R*log 2 (F), where F is the number of unique good tuples &lt;term, definition&gt; the pattern extracts, N is the total number of tuples (good and bad) and R=F/N. A pattern can extract both &lt;term, definition&gt; pairs as well as &lt;term, n-n-definition&gt; pairs. The first is considered a good tuple. The latter form, which is not a true &lt;term, definition&gt; pair is considered a bad tuple. 
   Examples of new patterns extracted using bootstrapping are set forth in the table below. 
   
     
       
             
             
           
         
             
                 
                 
             
             
                 
               Examples of Patterns Identified by 
             
             
                 
               Bootstrapping Algorithm 
             
             
                 
                 
             
           
           
             
                 
               &lt;term&gt; is characterized by &lt;definition&gt; 
             
             
                 
               &lt;term&gt;, in which &lt;definition&gt; 
             
             
                 
               &lt;term&gt; - in which &lt;definition&gt; 
             
             
                 
               &lt;term&gt;, which is &lt;definition&gt; 
             
             
                 
               &lt;term&gt; is used to &lt;definition&gt; 
             
             
                 
               &lt;term&gt; occurs when &lt;definition&gt; 
             
             
                 
               In &lt;term&gt;, &lt;definition&gt; 
             
             
                 
                 
             
           
        
       
     
   
   After new patterns are identified, they are applied to the corpus of full text articles (step  840 ) and new &lt;term, definition&gt; tuples are identified (step  850 ). These tuples are added to the temporary bootstrap dictionary (step  860 ) and then additional iterations can be performed a fixed number of times, such as three times, or until less than a predetermined number of new tuples is identified (step  870 ). 
   After the iteration process ends, the original dictionary is then merged with the boot strapping dictionary to produce the final output dictionary (step  880 ). 
   An alternate embodiment of the present systems and methods for extracting &lt;term, definition&gt; pairs from full text sources is illustrated in  FIGS. 9  through  11 . The system of  FIG. 9  is similar to the architecture depicted in  FIG. 1  except that instead of routing all sentences through the shallow parsing block  120  of  FIG. 1 , a module selection logic block  915  is used to route the sentences to either one or both of a pattern analysis  910  and/or a grammar analysis processing block  925 . Thus the embodiment of  FIG. 9  replaces the linguistic mark-up block  915  with the module selection logic block  915  and modifications to the shallow parsing block are also provided to account for this change. 
   Except as noted below, processing blocks  905 ,  910 ,  925 ,  930 ,  935 ,  940 ,  945  and  950  are substantially the same as processing blocks  105 ,  110 ,  125 ,  130 ,  135 ,  140 ,  145  and  150 , respectively, which are described above in connection with  FIG. 1 . 
   In step  905 , the articles, in computer readable form, such as ASCII, HTML and the like, are input to the system. The articles are passed to a preprocessing algorithm  910 , which tokenizes the input articles and formats the articles in a manner which is suitable for processing by text parser and part-of-speech tagging algorithms. The preprocessing operations of step  910  can include stripping away HTML tags other than those which are emphasis tags, such as &lt;EM&gt; and &lt;B&gt;, tokenizing the text and rewriting the text file as one sentence per line with each line numbered and identified to its source text. Tokenizing the text generally includes separating, by a space, each unit of a sentence (word, punctuation, number) that can be considered an independent token. Generally, hyphenated words are kept as a single token 
   Following preprocessing of step  910 , the system performs a module selection operation in module selection logic block  915 . The module selection block analyzes the input articles to route the text to either a pattern analysis processing block  920 , a grammar analysis processing block  925  or both processing blocks. The pattern analysis processing block  920  extracts term-definition pairs from the sentences routed to this processing block by the module selection logic  915  and places the term-definition pairs in a pattern dictionary database  930 . The operation of the pattern analysis processing block  920  is further described below in connection with  FIG. 10 . 
   Referring to  FIG. 10 , the operation of the module selection logic block  915  is further described. In the module selection logic, the input sentences are evaluated to determine if they are emphasized sentences (step  1010 ). In the case of HTML formatted files, emphasized sentences are identified by the presence of emphasis tags, such as &lt;EM&gt; and &lt;B&gt;. Those sentences which are identified as emphasized are passed to both the pattern analysis processing block  920  and the grammar analysis processing block  925  in step  1015  to extract &lt;term, definition&gt; tuples. 
   Those sentences from the input articles which are not emphasized sentences are further evaluated to determine whether the sentences include text markers which are indicative of the presence of a definition or cue phrases which are indicative of the presence of a definition (step  1020 ). Text markers that have been found to be indicative of a definition include hyphens, such as --, and parenthetical expressions following noun phrases. Cue phrases which are indicative of a definition include forms of “X is the term used to describe Y,” as opposed to phrases such as “for example” or “such as” which tend to indicate explanation rather than definition. Those sentences which are found to include cue phrases and/or text markers are passed to the pattern analysis block  920  in step  1025  which performs shallow parsing of the sentence to extract simple &lt;term, definition&gt; tuples. 
   Those sentences which do not include text markers or cue phrases in step  1020  are passed to the grammar analysis block  925  for full parsing and grammar analysis (step  1030 ). 
     FIG. 11  is a simplified flow chart illustrating an embodiment of the logical flow used in the pattern analysis (shallow parsing) block  920 . For each file that is passed to the pattern analysis processing block, part-of-speech (POS) tagging is applied to assign a part of speech label to each word (step  1110 ). A number of known POS tagging programs can be used in connection with step  1110 . One suitable POS tagging program is described by E. Brill, “A Simple Rule-based Part of Speech Tagger,” Proceedings of the Third Conference on Applied Natural Language Processing, Trento, Italy, 1992, the disclosure of which is hereby incorporated by reference in its entirety. 
   Following POS tagging, a noun phrase identification operation, referred to in the art as Noun Phrase Chunking, is applied in step  1120  to identify various forms of noun phrases in the sentences being processed. A suitable method for performing Noun Phrase Chunking is described by Ramshaw and Marcus in “Text Chunking Using Transformation-Based Learning,” Proceedings of Third ACL Workshop on Very Large Corpora,” MIT, 1995, the disclosure of which is hereby incorporated by reference in its entirety. The Noun Phrase Chunking algorithm identifies noun phrases in the sentences being analyzed and sets the noun phrases apart from the other text, such as by insertion of brackets. For example, the sentence “Arrhythmia—irregular heartbeat—is experienced by millions of people in a variety of forms.” will be tagged as follows:
     [Arrhythmia/NNP]--/: [irregular/JJ heartbeat/NN]--/: is/VBZ experienced/VBN by/IN [millions/NNS] of/IN [people/NNS] in/IN [a/DT variety/NN] of/IN forms/NNS;
 
where NNP represents a proper noun, JJ represents an adjective, NN represents common noun, VBZ is verb, present tense, singular, VBN is verb, past participles, IN means preposition, DT refers to determiner and NNS refers to common noun, plural.
   

   Following the Noun Phrase Chunking operation of step  1120 , for each sentence that contain text markers in step  1130 , a set of filtering rules will be applied in step  1140  to remove sentences that include misleading patterns which have been found are not indicative of term-definition pairs. The filtering rules generally will remove sentences which have conjunctions at the beginning of a text marker. In addition, the filtering rules will identify and remove phrases that indicate explanation rather than definition, such as “for example,” “for instance” and the like. The filtering rules can also identify and eliminate sentences that have a series of commas and conjunctions, which indicate enumeration and have not been found to identify term-definition pairs. It will be appreciated that additional rules may be found to be useful in identifying and eliminating phrases set off by text markers which are not indicative of term-definition pairs and that such rules could also be implemented in step  1140 . 
   Following the filtering operations of step  1140 , the remaining sentences including text markers are analyzed to identify noun phrase patterns in the form: Noun Phrase  1  {text marker} Noun Phrase  2  {text marker or.} (step  1160 ). In this pattern, either Noun Phrase  1  or Noun Phrase  2  may be either the term or definition. To identify the term and definition, for each such noun phrase pattern which represents a term-definition pair, the frequency of occurrence of Noun Phrase  1  and Noun Phrase  2  are determined. The noun phrase having the higher frequency of occurrence is designated the term and the other noun phrase is considered the definition for the term (step  1165 ). The &lt;term, definition&gt; tuples can be used to form a hash array where the terms are keys to the array and the definitions are the values of the array. The hash array is then added to the pattern dictionary database  930 . 
   Returning to step  1130 , those sentences which do not include text markers are passed to step  1170  which evaluates the input sentences to identify cue phrases within the sentences. A non-exhaustive list of cue phrases includes: “is the term used to describe”, “is defined as”, “is called” and the like. The sentence is then parsed by identifying the context on the left hand side (LHS) of the cue phrase and the context on the right hand side (RHS) of the cue phrase (step  1180 ). If the left hand side is a noun phrase, then the noun phrase will be considered a term and the right hand side will be considered a definition for the term (step  1190 ). The &lt;ten, definition&gt; pair can be added to a hash array and added to the pattern dictionary database  930 . 
   The methods described herein are generally embodied in computer programs. The programming language and computer hardware on which the methods are performed is not critical to the present invention. It will be appreciated by those skilled in the art that such programs are embodied on computer readable media, such as optical or magnetic media, such as CD-ROMS, magnetic diskettes and the like. Such programs can also be distributed by downloading the programs over a digital data network. 
   The systems and methods described herein provided for the automatic generation of dictionary entries based on an analysis of full text materials. When a corpus of domain specific full text materials are provided, a domain specific dictionary, such as a dictionary of technical terms, can be generated. The dictionary can be dynamic, with new entries being added when additional full text materials are input to the system to extract &lt;term, definition&gt; pairs. 
   Although the present invention has been described in connection with specific exemplary embodiments, it should be understood that various changes, substitutions and alterations can be made to the disclosed embodiments without departing from the spirit and scope of the invention as set forth in the appended claims.