{ "paper_id": "M93-1024", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T03:14:26.647762Z" }, "title": "Description of the LINK System Used for MUC-5", "authors": [ { "first": "Steven", "middle": [ "L" ], "last": "Lytinen", "suffix": "", "affiliation": { "laboratory": "Artificial Intelligence Laboratory", "institution": "The University of Michigan Ann Arbor", "location": { "postCode": "4810 9", "region": "MI" } }, "email": "" }, { "first": "Robert", "middle": [ "R" ], "last": "Burridge", "suffix": "", "affiliation": { "laboratory": "Artificial Intelligence Laboratory", "institution": "The University of Michigan Ann Arbor", "location": { "postCode": "4810 9", "region": "MI" } }, "email": "" }, { "first": "Peter", "middle": [ "M" ], "last": "Hastings", "suffix": "", "affiliation": { "laboratory": "Artificial Intelligence Laboratory", "institution": "The University of Michigan Ann Arbor", "location": { "postCode": "4810 9", "region": "MI" } }, "email": "" }, { "first": "Christian", "middle": [], "last": "Huyck", "suffix": "", "affiliation": { "laboratory": "Artificial Intelligence Laboratory", "institution": "The University of Michigan Ann Arbor", "location": { "postCode": "4810 9", "region": "MI" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "BACKGROUND Over the past five years, we have developed a natural language processing (NLP) syste m called LINK. LINK is a unification-based system, in which all syntactic and semantic analysi s is performed in a single step. Syntactic and semantic information are both represented in th e grammar in a uniform manner, similar to HPSG (Pollard and Sag, 1987). LINK has been used in several information extraction applications. In a project with General Motors, LINK was used to process terse free-form descriptions of symptoms displayed b y malfunctioning automobiles, and the repairs which fixed them. In this very narrow domain , LINK achieved recall and precision rates of 80-85%. Most recently, we used the LINK system to participate in MUC-4. During this competition , we developed initial versions of pre and postprocessing modules which were further developed in MUC-5. In MUC-4, LINK achieved recall and precision rates of about 40% .", "pdf_parse": { "paper_id": "M93-1024", "_pdf_hash": "", "abstract": [ { "text": "BACKGROUND Over the past five years, we have developed a natural language processing (NLP) syste m called LINK. LINK is a unification-based system, in which all syntactic and semantic analysi s is performed in a single step. Syntactic and semantic information are both represented in th e grammar in a uniform manner, similar to HPSG (Pollard and Sag, 1987). LINK has been used in several information extraction applications. In a project with General Motors, LINK was used to process terse free-form descriptions of symptoms displayed b y malfunctioning automobiles, and the repairs which fixed them. In this very narrow domain , LINK achieved recall and precision rates of 80-85%. Most recently, we used the LINK system to participate in MUC-4. During this competition , we developed initial versions of pre and postprocessing modules which were further developed in MUC-5. In MUC-4, LINK achieved recall and precision rates of about 40% .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "The tokenizer produces LISP-readable files from a 100-article source file . Each file consists of header information followed by the sentences of the article represented as lists of tokens . Token s that have special meaning in LISP, such as the single and double quotes, commas, and period s are modified to be readable by the main parsing engine .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tokenize r", "sec_num": null }, { "text": "Sentence boundaries are hypothesized whenever a period is seen . An exception to this is i f a period follows a known abbreviation, and is not followed by a capitalized token, then it is no t the end of the sentence .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tokenize r", "sec_num": null }, { "text": "Double quotes are simply removed, and single quotes that are used as quotation marker s are removed . Contractions are expanded and possessives are made into separate tokens (e .g., The Tokenizer checks the case of each word, and puts sequences of capitalized words insid e strings for the use of the Tagger, as described below . It also breaks apart hyphenated tokens i f the first half is a number (e .g., \"25-Mhz\"), to allow the grammar access to the units .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tokenize r", "sec_num": null }, { "text": "The Tokenizer also performs some filtering tasks . Names of locations at the beginning o f the text or abstract are removed, as are author name lines, and COMLINE tag lines . Sentence s that are too short to be interesting are removed .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tokenize r", "sec_num": null }, { "text": "Because the input is mixed case in this domain, and because many of the proper names tha t would normally be unknown to the system lexicon are capitalized, the MUC-5 LINK syste m uses a pre-parse tagger to process and attempt to identify capitalized words which are passe d as strings from the Tokenizer . The Tagger uses heuristics (aka hacks) to break apart strings i n several different ways . Some of the tags that are used include : :COMP-NAME for things that seem to be obviously company names, :LOCATION for city/state pairs, :PERSON-NAME for people names (if they have Mr, Mrs, VP, Dr in front), and :NAME for other names .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tagger", "sec_num": null }, { "text": "Some example rules that the tagger uses are :", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tagger", "sec_num": null }, { "text": "1. If a word is a known acronym (e .g. DRAM) or an abbreviation that is normally capitalized (e.g. \"Mbit\"), then just pass the word as a regular lexeme .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tagger", "sec_num": null }, { "text": "3. If a string is followed by a word like \"President\" or \"Spokesman\" and then another string , make the first part a company name and the rest a person name .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tagger", "sec_num": null }, { "text": "4. If a string is followed by a comma and then a state name, tag it as a city / state pai r", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Tagger", "sec_num": null }, { "text": "Our filtering mechanism allows the system to ignore all sentences which have no useful meaning. Each sentence in an article is checked to see if it contains at least one word whose meanin g is relevant to the domain ; if so, the sentence is passed on to the parsre . Words with relevan t meanings to this domain included verbs indicating the development or purchase of a microelectronics capability (e.g., \"transfer\" or \"use\") ; names of companies or people ; and various nouns of interest (e .g., \"device\", \"hydrofluoric\", \"temperature\" and \"DRAM\") .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Filter", "sec_num": null }, { "text": "LINK is unification-based chart parser, which parses a sentence at a time . The LINK parser applies unification grammar rules to a sentence to generate a syntactic and semantic representation. A set of principled grammar rule application heuristics select which grammar rules t o apply. If these heuristics fail, we revert to bottom-up chart parsing . We will outline the forma t of the grammar and then we will describe our parsing strategy .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The LINK parser", "sec_num": null }, { "text": "LINK's grammar rules are quite similar in form to those used in PATR-II (Shieber, 1986) . Semantic information resides mainly in the lexicon, along the lines of HPSG (Pollard and Sag , 1987) . This organization improves the portability of the system, since the vast majority o f the grammar should be applicable to other domains, while the lexicon contains most of th e domain-specific information .", "cite_spans": [ { "start": 72, "end": 87, "text": "(Shieber, 1986)", "ref_id": "BIBREF2" }, { "start": 166, "end": 190, "text": "(Pollard and Sag , 1987)", "ref_id": "BIBREF3" } ], "ref_spans": [], "eq_spans": [], "section": "The LINK grammar", "sec_num": null }, { "text": "The integration of syntactic and semantic knowledge into the same grammar formalism i s crucial to our system's ability to process large texts in a reasonable length of time, and t o producing the semantic analysis used to generate templates .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The LINK grammar", "sec_num": null }, { "text": "Edges are placed in the chart to represent constituents that the parser identifies . Edges hav e associated with them both syntactic and semantic information, represented in the form of a directed acyclic graph (DAG) . The DAGs correspond to the information in the set of gramma r rules used to build a constituent .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The LINK grammar", "sec_num": null }, { "text": "LINK is a bottom-up chart parser which does not use top-down constraints . Top-down constraints are not used so that as many partial parses as possible can be generated .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The MUC-5-LINK parsing strateg y", "sec_num": null }, { "text": "Because unrestricted bottom-up chart parsing can be (and is, in our system) very inefficient , LINK uses heuristics to decide on the next edge to be entered into the chart . Many of the heuristics we use are taken from those suggested in psycholinguistic work (e .g., Ford, Bresnan , and Kaplan, 1982 ), although we found the need to embellish these with additional heuristics o f our own (see Huyck and Lytinen, 1993, for details) .", "cite_spans": [ { "start": 268, "end": 300, "text": "Ford, Bresnan , and Kaplan, 1982", "ref_id": "BIBREF0" }, { "start": 394, "end": 431, "text": "Huyck and Lytinen, 1993, for details)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "The MUC-5-LINK parsing strateg y", "sec_num": null }, { "text": "The heuristics are encoded in a rule-based system . The rules are invoked each time a ne w edge is to be entered into the chart, in case more than one edge could be entered next . Each rule specifies a set of conditions under which a grammar rule should be preferred or unpreferred . Rules may specify several different types of preference levels, similar to the preferences that ar e used in SOAR (Laird, Rosenbloom, and Newell, 1987) . Heuristics may state that one grammar rule is preferable to another under some set of circumstances (i .e., if it is possible to apply both rule a and rule b at this point, then rule a should be applied), that a rule is a good candidate , that it is a bad candidate, or that it is the best candidate (i .e., under these conditions, definitel y apply this grammar rule) .", "cite_spans": [ { "start": 398, "end": 435, "text": "(Laird, Rosenbloom, and Newell, 1987)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "The MUC-5-LINK parsing strateg y", "sec_num": null }, { "text": "Because the heuristics are incomplete, often it is the case that, at some point during th e parse, they are not able to suggest which rule to apply next . When this occurs, the syste m performs regular undirected bottom-up parsing . This continues until a complete parse of th e sentence is found, no more rules can be applied, or a maximum time limit is exceeded . If a complete parse is not found, one or more partial parses is passed on for further processing . No attempt is made to \"patch\" together a complete interpretation of the sentence if it is not parse d successfully.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The MUC-5-LINK parsing strateg y", "sec_num": null }, { "text": "The postprocessor is responsible for assembling the semantic representations of individua l sentences into a coherent representation of the entire article, and for generating the respons e template(s) from this overall representation . Our MUC-5 postprocessor is a two-stage, rulebased system . In the first stage, the rules transform representations produced by the parser into a cannonical form . Irrelevant portions of the representation are also discarded in this firs t stage. In the second stage, another set of rules transforms these representations into a for m which much more closely resembles the form of the response templates .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Postprocesso r", "sec_num": null }, { "text": "A rule consists of a left hand side (lhs), which must match (i .e., unify with) the semanti c output from the parser. If the lhs matches, the representation is converted to the form specifie d in the right hand side (rhs) .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Postprocesso r", "sec_num": null }, { "text": "Here are some example rules from the first stage of postprocessing :", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Postprocesso r", "sec_num": null }, { "text": "(CONVERT report-action (1hs) = report (lhs object) = ACTIO N (rhs) = (lhs object ) (rhs actor) = (lhs actor) ) The first rule converts the representation produced for sentences such as \"It was reporte d that . . .\" . If the main predicate representing the sentence is REPORT, and the reported object i s an ACTION, then this rule discards the REPORT predicate and replaces it with the ACTION . If the ACTION has no actor, it is filled in as the actor of the REPORT . Thus, the transforme d representation of the sentence \"LSI Logic Corp. reported that they developed . . .\" becomes DEVELOP, with the actor filled in as \"LSI Logic Corp .\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Postprocesso r", "sec_num": null }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "(CONVERT equi v", "eq_num": "(" } ], "section": "The Postprocesso r", "sec_num": null }, { "text": "The second rule transforms the representation of a sentence such as \"The customer wa s Hampshire Instruments .\" Whenever the main predicate is EQUIV (our semantic representatio n of \"to be\"), and the subject (or actor) of this action is \"customer\", this rule converts th e representation to the predicate TRANSFER-TO-CUSTOMER, the recipient of which is th e complement (object) of \"to be\" . Together, these two rules transform the representation of a sentence like \"The customer is reported to be LSI Logic Corp\" to the predicate TRANSFER-TO-CUSTOMER, the recipient of which is \"LSI Logic Corp.\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Postprocesso r", "sec_num": null }, { "text": "The postprocessor also merges representations from separate sentences into a single template when appropriate . After the transformation rules are run, the representations of two sentence s are merged together if they can unify. The resulting single representation is simply the resul t of the unification . If representations of sentences cannot be unified, then their representation s may produce separate templates in the response .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Postprocesso r", "sec_num": null }, { "text": "We now describe our system's processing of the walkthrough article, 2789568 :", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "In the second quarter of 1991, Nikon Corp . (7731) plans to market the \"NSR-1755EX8A,\" a new stepper intended for use in the production of 64-Mbit DRAMs . The stepper will use an 248-nm excimer laser as a light source and will have a resolution of 0 .45 micron, compared to the 0 .5 micron of the company's latest stepper . Nikon will price the excimer laser stepper at 300-350 million yen, and the compan y expects to sell 50 systems during the initial year of marketing .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "The response generated by LINK for this article and the answer key are shown in figures 2 and 3 .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "We will describe the behavior of each module on the example article . The tokenized walkthrough file is shown below:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "(who-templated 0 ) (document-no (2789568) ) (date (October 19 1,1 1990 ) (In the second quarter of 1991 1,1 \"Nikon Corp\" 1(I 7731 1)1 plans to market the \"NSR-1755EX8A\" 1,1 a new stepper intended for use in the production of 64 \"Mbit DRAMs\" 1 .1 ) (The stepper will use an 248 nm excimer laser as a light source and will hav e a resolution of 0 .45 micron 1,1 compared to the 0 .5 micron of the company I'SI latest stepper 1 .1 ) (Nikon will price the excimer laser stepper at 300 to 350 million yen 1,1 and the company expects to sell 50 systems during the initial year of marketin g I I )", "cite_spans": [], "ref_spans": [ { "start": 50, "end": 72, "text": "(October 19 1,1 1990 )", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "All three of the sentences from the walkthrough example are passed through the filter fo r further processing . The first sentence mentions \"Nikon Corp\" and has other meaningful words ; the second sentence has the word \"use\" and other meaningful words ; and the third sentence has the word \"company\" along with other meaningful words . The tagger has used the company indicator \"Corp\" to specify \"Nikon Corp\" as a compan y name . \"NSR-1755EX8A\" was not in the lexicon, nor did it have any additional indicators, s o it was assumed (correctly) to be a proper name . The string \"Mbit DRAMs\" was not tagge d because each word is known to the tagger to be an acronym / abbreviation . These words are simply passed along, and the lexicon provides the appropriate information for them .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "Before parsing, the chart for the parser (as described below) is built adding constituents fo r each word or tagged item . When the parser reads a tagged item from the input sentence, i t simply makes an entry in the chart at that position with the semantic type corresponding to th e tag and the words contained in the item . For example, ( :COMP-NAME NIKON CORP) turn s into an entry with type Company, and name \"Nikon Corp\" .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "The parser is not successful at completely parsing any of these sentences . This primaril y because the grammar and lexicon are lacking several necessary pieces of information . In the first sentence, \"plan\" is not marked in the lexicon as taking an infinitival complement . Thus, th e construction cannot be parsed . There is also no grammar rule for parsing a determiner followe d by a name as a noun phrase (\"the NSF-1766EX8A\") . Had this sentence read, \" . . . market th e NSF-1766EX8A stepper,\" the partial parse would have been more complete . As it is, only th e following information can be extracted from this sentence : Except for \"market\", none of the verbs in this sentence were defined in our lexicon a s interesting ; thus, none of them are included in the partial parses sent on to the postprocessor .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "Because \"Nikon Corp\" and the name of the stepper are not attached to anything, the postprocessor does not know where in the final template these should be placed . Thus, they ar e discarded . STEPPER, however, results in the production of a LITHOGRAPHY template, an d the DRAM is attached as the DEVICE, resulting in the response shown in figure 2 .", "cite_spans": [], "ref_spans": [ { "start": 339, "end": 347, "text": "figure 2", "ref_id": "FIGREF2" } ], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "No additional information is extracted from sentences 2 and 3 . In sentence 2, the tex t \"will have a resolution of 0 .45 micron, compared to the 0 .5 micron of the company's lates t stepper\" was not parsed well enough for the system to realize that 2 different steppers are bein g described . Granularity specifications were not handled well by the postprocessing rules . Had the granularities been successfully attached to the representations of the two steppers, the n our system would have produced two different LITHOGRAPHY templates, because differen t granularities would have caused unification of the two steppers to fail . Thus, the respons e would have contained two separate templates . However, the granularities were not successfull y incorporated into the templates, resulting the steppers being merged into a single template .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "The final sentence provides another opportunity to identify \"Nikon Corp\" as being the MAN-UFACTURER and DISTRIBUTOR of the LITHOGRAPHY technique . However, again, th e word \"price\" was not defined in our lexicon as a verb relevant to the domain, so the informatio n was ignored .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "SYSTEM WALKTHROUGH", "sec_num": null }, { "text": "The LINK system's performance on the MUC-5 English microelectronics test set is shown i n figure 4 . Our system's performance is relatively precision-oriented . We suspect that this is du e to the fact that our approach attempts complete analyses of each sentence . Thus, informatio n which is extracted is relatively reliable, while additional information may be missed . Figure 4 : Performance of LINK on the MUC-5 English Microelectronics test set", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ANALYSIS OF PERFORMANC E", "sec_num": null }, { "text": "Our system was tunable its use of partial parses that were used to generate templates . In it s most conservative setting, only partial parses whose semantic interpretations involved importan t actions (e .g ., DEVELOP, SELL, etc.) were used in postprocessing . The system could be mad e less conservative by expanding the types of partial parses that were used in tempalte generation . In its least conservative setting, even single words might be chosen as interesting partial parses , resulting in the generation of a template . For example, the appearance of the word \"CVD \" could result in the generation of a LAYERING template with TYPE field CVD .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ANALYSIS OF PERFORMANC E", "sec_num": null }, { "text": "For the test run, we used the system in its least conservative setting . During development testing, we found that this setting resulted in approximately 50% improvement in recall rate s without adversely affecting precision. We believe that this reflects the English microelectronic s domain. Since the vocabulary used in articles in this domain consisted of a large number of technical terms not normally used in most English texts, the extraction of information base d on occurrence of these words without analysis of their surrounding context was a relatively saf e thing to do . In other domains, it is likely that the use of single-word partial parses would resul t in significant reduction in precision.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ANALYSIS OF PERFORMANC E", "sec_num": null }, { "text": "Our system's precision results did suffer from the fact that templates were sometimes produced that contained so little information that they could not be matched by the scorer t o answer key templates. These templates were counted by the scorer as spurious, reducing ou r precision score . We plan to analyze our results further to calculate the system's precision ha d it not produced these unmatchable templates .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ANALYSIS OF PERFORMANC E", "sec_num": null }, { "text": "Of interest is our system's performance on text filtration . The 99% recall, 75% precision performance is much higher than what might be expected given LINK's overall recall/precision rates . We suspect that these results are due to our system's full-analysis approach .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ANALYSIS OF PERFORMANC E", "sec_num": null }, { "text": "Our system is far from mature . Due to lack of resources this year, the total developmen t time for the system totaled only about 6 person-months . This represents about 1/3 of the development time of our MUC-4 system . Thus, the knowledge base of the system is still quit e incomplete. This resulted in the low recall performance of the system . Further development of the knowledge base is likely to greatly improve system performance .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ANALYSIS OF PERFORMANC E", "sec_num": null }, { "text": "We used two specialized techniques to aid in the development of the system knowledge bases . The first was to use the development keys as a sort of pocket dictionary for some of th e important and often-used words . We did this by extracting all the slot fillers and their type s from the templates . For all the string fills, we added the string directly to the dictionary wit h the semantic type that was derived from the slot that it filled . Many of the set fills were also added verbatim to the lexicon, since in this domain set fills were often technical terms (e .g. , CVD). Other lexicon entries were simply created by either expanding the set-fill abbreviation s or abbreviating the full-text set-fills .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "System Training", "sec_num": null }, { "text": "The other main training source came as a result of the tagger . Since the tagger made it possible to recognize proper names that were not in the lexicon by analyzing strings of capitalize d words, we used the tagged items to hypothesize new lexicon entries . This was only done fo r items that the tagger was sure of, like company names (strings that ended with \"Corp\", \"Co\" , \"Inc\", etc) and person names that started with \"Mrs\", \"Dr\", \"VP\", etc . These definitions were not entered directly into the lexicon, but were put into a separate file so that they could b e reviewed by a knowledge engineer .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "System Training", "sec_num": null }, { "text": ". If the string ends in a word like \"Corp\" or \"Co \" , tag the string as a company name.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "Although LINK's performance on the English Microelectronics testset was less then stellar , it is difficult to draw conclusions about the use of our approach on this domain . The primary reason for degradation of performance as compared to MUC-4 was a lack of resources needed t o develop a proper knowledge base for the domain . Lack of information in the lexicon, grammar , and system's domain knowledge resulted in poor analysis of the majority of articles .The system's relatively good performance in precision indicates that our full-analysis approach is likely to yield reliable results when information is extracted . However, the Microelectronics domain may be unusual in the frequency of technical terms which are not commonly use d in general English. Because of this property of the domain, it appears that techniques relying o n less complete analysis of the text may be appropriate also . Our own experience indicated tha t the utilization of even partial parses of only a few words (or even a single word) improved ou r system's recall without damaging precision . We believe that in a domain with a less specialize d vocabulary, techniques relying on specific keywords would be more likely to degrade precisio n performance .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "annex", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "The Mental Representation of Grammatical Relations", "authors": [ { "first": "M", "middle": [], "last": "Ford", "suffix": "" }, { "first": "J", "middle": [], "last": "Bresnan", "suffix": "" }, { "first": "Kaplan", "middle": [ "R" ], "last": "", "suffix": "" } ], "year": 1982, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Ford, M ., Bresnan, J ., and Kaplan . R. (1982). A competence-based theory of syntactic closure . In Bresnan, J . (ed), The Mental Representation of Grammatical Relations. Cambridge, MA : MIT Press.", "links": null }, "BIBREF1": { "ref_id": "b1", "title": "Efficient heuristic natural language parsing", "authors": [ { "first": "C", "middle": [], "last": "Huyck", "suffix": "" }, { "first": "S", "middle": [], "last": "Lytinen", "suffix": "" } ], "year": 1993, "venue": "Proceeding s of the Eleventh National Conference on Artificial Intelligence", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Huyck, C ., and Lytinen, S . (1993) . Efficient heuristic natural language parsing . In Proceeding s of the Eleventh National Conference on Artificial Intelligence, Washington, D .C., July 1993 .", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "An Introduction to Unification-Based Approaches to Grammar", "authors": [ { "first": "S", "middle": [], "last": "Shieber", "suffix": "" } ], "year": 1986, "venue": "Center for the Study of Language and Information", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Shieber, S . (1986) . An Introduction to Unification-Based Approaches to Grammar . Stanford, CA: Center for the Study of Language and Information .", "links": null }, "BIBREF3": { "ref_id": "b3", "title": "Information-based Syntax and Semantics", "authors": [ { "first": "C", "middle": [], "last": "Pollard", "suffix": "" }, { "first": "I", "middle": [], "last": "Sag", "suffix": "" } ], "year": 1987, "venue": "Cente r for the Study of Language and Information", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Pollard, C ., and Sag, I. (1987) . Information-based Syntax and Semantics . Stanford, CA : Cente r for the Study of Language and Information .", "links": null } }, "ref_entries": { "FIGREF0": { "uris": null, "num": null, "text": "Modules of the MUC-5 LINK syste m \"Nikon's'. --\"Nikon *'S*\") . 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