| { | |
| "paper_id": "M93-1018", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:14:33.493001Z" | |
| }, | |
| "title": "SRA: DESCRIPTION OF THE SOLOMON SYSTEM AS USED FOR. MUC-5", | |
| "authors": [ | |
| { | |
| "first": "Chinatsu", | |
| "middle": [], | |
| "last": "Aone", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Sharon", | |
| "middle": [], | |
| "last": "Flank", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Doug", | |
| "middle": [], | |
| "last": "Mckee", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Paul", | |
| "middle": [], | |
| "last": "Kraus", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
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| "abstract": "SRA used a language-independent, domain-independent, multipurpose text understanding system as the cor e of the MUC-5 system for extraction from English and Japanese joint venture texts. SRA's NLP core system , SOLOMON, has been under development since 1986. It has been used for a variety of domains, and wa s aimed from the start to be language-independent, domain-independent, and application-independent. Mor e recently, SOLOMON has been extended to be multilingual, beginning with Spanish in 1990 and Japanese i n 1991. The Spanish-Japanese text understanding system that uses SOLOMON was developed for a dornai n very different from the MUC-5 joint venture domain (cf. Aone, et al. [2]). SOLOMON's principal applications have been in data extraction, but it is also used in a prototyp e machine translation system (cf. Aone and McKee [5]). The domain areas in which SOLOMON application s have been developed are : financial, terrorism, medical, and the MUC-5 joint-venture domain. SRA has significantly enhanced its capability to add new domains and languages by developing new strategies fo r data acquisition using both statistical techniques and a variety of user-friendly tools. MUC-5 SYSTEM ARCHITECTUR E SOLOMON employs a modular, data-driven architecture to achieve its language-and domain-independence. The MUC-5 system, which uses SOLOMON as a core engine, consists of seven processing modules an d corresponding data modules, as shown in Figure 1, which will be described in the following sections .", | |
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| "text": "SRA used a language-independent, domain-independent, multipurpose text understanding system as the cor e of the MUC-5 system for extraction from English and Japanese joint venture texts. SRA's NLP core system , SOLOMON, has been under development since 1986. It has been used for a variety of domains, and wa s aimed from the start to be language-independent, domain-independent, and application-independent. Mor e recently, SOLOMON has been extended to be multilingual, beginning with Spanish in 1990 and Japanese i n 1991. The Spanish-Japanese text understanding system that uses SOLOMON was developed for a dornai n very different from the MUC-5 joint venture domain (cf. Aone, et al. [2]). SOLOMON's principal applications have been in data extraction, but it is also used in a prototyp e machine translation system (cf. Aone and McKee [5]). The domain areas in which SOLOMON application s have been developed are : financial, terrorism, medical, and the MUC-5 joint-venture domain. SRA has significantly enhanced its capability to add new domains and languages by developing new strategies fo r data acquisition using both statistical techniques and a variety of user-friendly tools. MUC-5 SYSTEM ARCHITECTUR E SOLOMON employs a modular, data-driven architecture to achieve its language-and domain-independence. The MUC-5 system, which uses SOLOMON as a core engine, consists of seven processing modules an d corresponding data modules, as shown in Figure 1, which will be described in the following sections .", | |
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| "section": "Abstract", | |
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| "text": "Inference is not performed if sentences and paragraphs are rigorously marked . The output is piped to a post-processor, which does a fast lookup of each word in a btree gazetteer, and includes entry information in the tokens of place names .", | |
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| "text": "Preprocessing consists of two processors, the morphological analyzer and the pattern matcher, and associate d data in the form of morphological data, lexicons, and patterns for each language . Its input is a tokenized message, and its output is a series of lexical entries with syntactic and semantic attributes .", | |
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| "section": "Preprocessin g", | |
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| "text": "Declarative morphological data for inflection-rich Japanese and Spanish is compiled into finite-stat e machines . The English domain lexicon was derived from development texts automatically, using a statistica l technique (cf. McKee and Maloney [10] ) . This derived lexicon also contains automatically acquired domainspecific subcategorization frames and predicate-argument mapping rules called situation types (cf. Aone ", | |
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| "text": "McKee [3] ), as shown in Figure 2 .", | |
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| "text": "Figure 2", | |
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| "text": "Pattern recognition handles a wide range of phenomena, including multi-words, numbers, acronyms , money, date, person names, locations, and organizations . We extended the Pattern matcher to handle multilevel pattern recognition . The pattern data are divided into ordered multiple groups called priority groups, and the patterns in each group are fired sequentially, avoiding recursive applications as much as possible . This extension speeded up the performance of Preprocessing significantly .", | |
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| "text": "The processor for Syntactic Analysis is a parser based on Tomita 's algorithm (cf. Tomita [11]), with modifications for disambiguation during parsing . Syntactic Analysis data consist of X-bar based phrase structur e grammars and preparse patterns for each of the three languages, English, Japanese, and Spanish . Syntacti c Analysis outputs F-structures (grammatical relations), along the lines of Lexical-Functional Grammar (cf . Bresnan [7] ), as shown in Figure 3 . The Semantic Interpretation module is interleaved for disambiguatio n Preparsing takes the burden off of main parsing and increases accuracy, by recognizing structures such a s sentential complements, appositives, certain PP's, etc . by pattern matching, and sending these to the parse r as chunks . These preparse chunks are parsed prior to main parsing using the same grammars, and thei r output consists of F-structures as well .", | |
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| "text": "Figure 3", | |
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| "section": "Syntactic Analysi s", | |
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| "text": "\u2022 Appositives : Or i~\"industry's largest Tokyo Kaijou \"", | |
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| "text": "\u2022 Sentences with certain verb endings :", | |
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| { | |
| "text": "' 7 X . ]I ~. WE . I", | |
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| "section": "Syntactic Analysi s", | |
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| "text": "\u2022 PP 's : start production [in january 1990] with production of 20,000 iro n", | |
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| "start": 27, | |
| "end": 44, | |
| "text": "[in january 1990]", | |
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| "text": "In order to test the progress of grammar development and pinpoint trouble spots, automatic evaluatio n of grammars was used . SRA adapted the community-wide program Parseval (cf. Black, et al . [6] ) for use in Japanese in addition to English . Testing on Japanese was limited, since there are not many brackete d Japanese texts to use as answer keys .", | |
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| "text": "Semantic Interpretation uses a language-independent processing module, and its data are predicate-argumen t mapping rules for each verb, plus both core and domain knowledge bases . Semantic Interpretation work s", | |
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| "section": "Semantic Interpretatio n", | |
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| "text": "A JAPANESE TRADING HOUSE . . . ) . Domain knowledge bases, on the other hand, were acquired manually . However, a new rapid knowledg e acquisition tool called KATooI was used to link a lexical entry to its corresponding semantic concept in th e knowledge bases (cf. Figure 5 ) .", | |
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| "end": 274, | |
| "text": "Figure 5", | |
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| "section": "BRIDGESTONE SPORTS CO . SAID FRIDAY IT HAS SET UP A JOINT VENTURE IN TAIWAN WITH A LOCAL CONCERN AN D", | |
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| "text": "If a full parse cannot be created, SOLOMON uses a fragment combination strategy . Debris Parsin g and its subsequent process, Debris Semantics, work together to obtain the best interpretation from sentenc e fragments . They use as data the grammars and knowledge bases, and they output semantic structures jus t like when a full parse is created . Debris Parsing retrieves the largest and most preferred constituents from the parse stack . It then reparses the rest of the input, and creates debris F-structures with the best fragmen t constituents . Debris Semantics relies on the semantic interpreter to process each fragment, and then fit s fragments together using semantic constraints on unfilled slots .", | |
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| "section": "[ST : <S > SUBJECT : [ST : <NP > HEAD : IT ] PREDICATE : [ST : <VP> TENSE : PRESENT ASPECT : PERFECT PREDICATE : (CREATE ) ROOT : SET VERB-PARTICLE : UP ] OBJECT : [ST : <HP > HEAD : A-JOINT-VENTURE] PREP-ARGS : ([ST : <PP > MARKED : WIT H HEAD : A-LOCAL-CONCERN-AND-A-JAPANESE-TRADING-HOUSE] ) ADJUNCTS : ([ST : <PP > MARKED : I H HEAD : TAIWAN])]] ]", | |
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| "text": "Discourse Analysis, which was redesigned and implemented this year (cf . Aone and McKee [4] ), performs reference resolution . Discourse Analysis uses a data-driven architecture to achieve language-independence , domain-independence, and extensibility . It employs a single language-independent, domain-independen t processor, and several discourse knowledge bases, some of which are shared among different languages . Th e output, of Discourse Analysis is a set of semantic structures with coreference links added, i .e . File Card s (cf. Heim [9] ) . Discourse phenomena handled for the joint venture domain include name anaphora (e .g . \\> ;", | |
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| "text": "x ,3 :33\\MAt'V Y The system traces for English and Japanese walkthrough examples are shown in Figure 6 and Figure 7 . In the English example, the two instances of name anaphora for \"Bridgestone Sports Co .\" are recognized , while in the Japanese example, all the references to \"Tokyo Kaijou Kasai Hoken, \" including appositives, ar e resolved .", | |
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| "text": "Figure 6", | |
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| "text": "Figure 7", | |
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| "text": "Pragmatic Inferencing performs reasoning in order to derive implicit information from the text, using a forward chainer and inference rules . Pragmatic Inferencing outputs semantic structures, with inferred inforinat ion added . It infers additional information from \"literal\" meanings as required for application domains . For instance, in the walkthrough example, in order to infer \"THE TAIWAN UNIT \" is a joint venture company frorr, the phrase \"THE ESTABLISHMENT OF THE TAIWAN UNIT\" the following rule is used . It is easy for developers to add, change or remove inferred information due to the declarative nature o f the inference rules . For instance, to get an additional tie-up from \"Company A and Company B tied wit h Company C \" , in ,t, ty-000''2, we just, had to add another rule to infer that. when companies \"tie,\" they form a tie-up . ", | |
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| "section": "Pragmatic Inferencin g", | |
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| "text": "The Extract module performs template generation, translating the domain-relevant portions of our languageindependent semantic structures into database records . We maintain a strong distinction between processin g and data even in template generation . Thus, we use the same processing module to output in differen t languages and to several database schemata, including to a flat template-style schema as in MUC-4 and t o a more object-oriented schema as in MUC-5 .", | |
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| "section": "Extract", | |
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| "text": "To do the actual template filling, we rely on Extract data made up of kb-object/slot to db-table/fiel d mapping rules and conversion functions for the individual values (e .g . set fills, string fills) . For example, th e #nationality slot of an #ORGANIZATION object in our knowledge base corresponds to the Nationalit y field of the Entity object in the MUC-5 template .", | |
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| "text": "SOLOMON is designed for reusability . Each processing module is data-driven and reusable in other languages and other domains, as well as in applications other than data extraction (e .g . machine translation , abstracting, summarization) . A large portion of the data is also reusable in : The data acquisition tools and techniques are also reusable in other languages and domains . The statistical techniques used to derive lexical information can be reused for other domains . LEXTooI, the lexicon acquisition tool, is multilingual and relies on system data files for category and morphological information . KBTooI, the knowledge base acquisition tool, is language-independent just as the knowledge bases ar e language-independent . KATool, the knowledge acquisition tool that links lexicon entries with the appropriate knowledge base concepts, is entirely data-driven as well, and is therefore completely reusable . Figure 8 summarizes the reusability of SRA ' s MUC-5 system .", | |
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| "text": "Figure 8", | |
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| "section": "REUSABILITY OF THE SYSTE M", | |
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| "text": "Our MUC-5 results for the English and Japanese joint-venture domain task are shown in Table 1 . We spen t 10 .55 person-months for this task, most of which were devoted to data development for both languages (se e Table 2 ) . The \"other\" category includes time spent on developing language-independent data such as a joint-venture domain knowledge base, pragmatic inference rules, and Extract data for template generation .", | |
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| "start": 86, | |
| "end": 93, | |
| "text": "Table 1", | |
| "ref_id": "TABREF1" | |
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| "start": 214, | |
| "end": 221, | |
| "text": "Table 2", | |
| "ref_id": "TABREF2" | |
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| "section": "TEST RESULTS AND ANALYSIS", | |
| "sec_num": null | |
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| "text": "We believe that the results do not indicate the potential of our system, since the system performance fo r both languages was still improving after five months of development . Much of the work we did resulted in long-term improvements to our overall text understanding capability, all of which will ensure a stronger base system for future applications . This implies that although the development cycle for data extraction system using a text understanding system may be slower in its current maturity stage, the potential for such a syste m is still unknown and represents a most promising avenue for development . We are particularly pleased wit h the success of our Japanese system : no other Japanese MUC-5 site is using the full understanding approach , but we did as well and our performance continues to improve )", | |
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| "section": "TEST RESULTS AND ANALYSIS", | |
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| "text": "Staff time was the major limiting factor . We needed more time to perform more testing and evaluation l In the 18-month Tipster evaluation, the highest JJV F-measure was about 40 . using the scoring program, and to finely tune Extract (template generation) mapping rules . We discovered we were hampered by formatting errors, and in addition considerable information was \"understood\" by th e system all the way through, but was not extracted by the template generator . Since the discourse modul e was new, it would have been helpful to have additional time to test and expand it . In addition, we neede d more time to fill the OWNERSHIP, REVENUE, and TIME objects, which we simply did not output .", | |
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| "section": "TEST RESULTS AND ANALYSIS", | |
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| "text": "Overall, the data-driven architecture in SOLOMON allowed for minimum work on processing modules whe n working on different languages and domains . We ported the system to Spanish in a week for the demonstration given, at the MUC-5 conference .", | |
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| "section": "CONCLUSION", | |
| "sec_num": null | |
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| { | |
| "text": "Although we successfully acquired large amounts of domain data from domain texts in both languages , using both statistical methods and newly developed user-friendly knowledge acquisition tools, we recogniz e the need to move even more quickly to new domains and languages . We plan to continue our work on automatic acquisition of lexicons, knowledge bases, and links between them in multiple languages .", | |
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| "section": "CONCLUSION", | |
| "sec_num": null | |
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| { | |
| "text": "Tuning performance of each module (e .g. parsing, discourse analysis) as well as the' performance o f the whole system to a particular task more rapidly is another research issue we identified . We believe that developing automatic evaluation and training algorithms for such automated module/system tuning is crucia l to develop a data extraction system that produces optimal results .", | |
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| "section": "CONCLUSION", | |
| "sec_num": null | |
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| ], | |
| "back_matter": [ | |
| { | |
| "text": "We are indebted to Rajeev Agarwal, Debbie Sanders, and Vera Zlatarski for their hard work and dedicatio n in data development, module testing, and more . We also gratefully acknowledge the contributions of Scot t Bennett, David Garfield, and Hatte Blejer to the MUC-5 process .", | |
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| "section": "ACKNOWLEDGEMENT S", | |
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| ], | |
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| "BIBREF0": { | |
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| "title": "Acquiring Predicate-Argument Mapping Information from Multilingual Texts", | |
| "authors": [ | |
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| "FIGREF0": { | |
| "num": null, | |
| "uris": null, | |
| "text": "MUC-5 System ArchitectureSentence and paragraph boundries are inferred using a conservative algorithm and marked as inferred .", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Statistically Acquired Lexical Entrie s of prepositional phrase attachment, conjunctions, and so on, by calling semantic functions, which are share d by all three languages, from inside the grammar .", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Simplified F-Structure Output by Syntactic Analysi s off of language-neutral F-structures in order to handle all the languages . It outputs semantic structures, i .e . predicate-argument and modification relations, as shown in Figure 4 . The predicate-argument mapping rule s (i .e . rules which map F-structures to semantic structures) are acquired automatically (cf . Aone and McKee [3]", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF3": { | |
| "num": null, | |
| "uris": null, | |
| "text": ". SAID FRIDAY IT HAS SET UP A JOINT VENTURE I I TAIWAN WITH A LOCAL CONCERN AND A JAPANESE TRADING HOUS E Semantic (Predicate-Argument) Structur e3\\ v\\~.\\J\\l :a~~~il:X25.", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Knowledge Acquisition Too l 211 DISCOURSE : Classified $<DISCOURSE-MARKER DISCOURSE-MARKER-181>(\"BRIDGESTONE SPORTS\") as DP-NAM E DISCOURSE : Found an exact match , ante : $(DISCOURSE-MARKER DISCOURSE-MARKER-83>(\"BRIDGESTONE SPORTS CO .\" ) ref : $<DISCOURSE-MARKER DISCOURSE-MARKER-181>(\"BRIDGESTONE SPORTS\" ) DISCOURSE : Classified $<DISCOURSE-MARKER DISCOURSE-MARKER-206>(\"BRIDGESTONE SPORTS\") as DP-NAM E DISCOURSE : Found an exact match , ante : $<DISCOURSE-MARKER DISCOURSE-MARKER-181>(\"BRIDGESTONE SPORTS\" ) ref : $(DISCOURSE-MARKER DISCOURSE-MARKER-206>(\"BRIDGESTONE SPORTS\" ) English Discourse Trace Exampl e => IMLEA:%glIg I)ISCOURSE : Classified #<DISCOURSE-MARKER DISCOURSE-MARKER-511>( \" 1 # , .Z*k. \" ) as DP-NAM E DISCOURSE : Found an exact match , ante : #<DISCOURSE-MARKER DISCOURSE-MARKER-: Classified #<DISCOURSE-MARKER DISCOURSE-MARKER-573>(\" 1#AE\") as DP-NAME DISC(.)URSE : Found an exact match , ante : #<DISCOURSE-MARKER DISCOURSE-MARKER-511>(\" \" ) ref : #<DISCOURSE-MARKER DISCOURSE-MARKER-573>(' , E\") Japanese Discourse Trace Exampl e \"BRJl)GESTONE SPORTS\" for \"BRIDGESTONE SPORTS CO .\") and definite NP's such as \"THE NE W ('OMPAN l", | |
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| }, | |
| "FIGREF5": { | |
| "num": null, | |
| "uris": null, | |
| "text": "defrule rule-0009 ((?event) (?event) ) :example (\"PNI and SRA established a new company .venture-company ?event ?x ) (in-jv-event ?x ?event)))", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF7": { | |
| "num": null, | |
| "uris": null, | |
| "text": "General pattern data (e .g . date, location, personal name, organization name ) Grammars Some of the discourse knowledge source s \u2022 Other language s -Domain knowledge bases", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF8": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Reusability of SRA ' s MUC-5 System -Some of the discourse knowledge sources -Inference rule s -Extract (template generation) dat a", | |
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| }, | |
| "TABREF1": { | |
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| "text": "SRA ' s Scores for the English and Japanese Joint Venture Domai n", | |
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