| { |
| "paper_id": "M93-1017", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T03:14:27.256189Z" |
| }, |
| "title": "PRC Inc. : DESCRIPTION OF THE PAKTUS SYSTE M USED FOR MUC-5", |
| "authors": [ |
| { |
| "first": "Bruce", |
| "middle": [], |
| "last": "Loatma", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Chih-King Yan g PRC Inc . Technology Division", |
| "location": { |
| "addrLine": "1500 PRC Drive McLean", |
| "postCode": "22102", |
| "region": "VA" |
| } |
| }, |
| "email": "loatman_bruce@po.gi" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "s. prc. cor n BACKGROUND The PRC Adaptive Knowledge-based Text Understanding System (PAKTUS) was develope d as an Independent Research and Development project at PRC from 1984 through 1992. It includes a core English lexicon and grammar, a concept network, processes for applying these to lexical , syntactic, semantic, and discourse analysis, and tools that support the adaptation of the generi c core to new domains, primarily by acquiring sublanguage and domain-specific lexicon and topic patterns. The lexical, syntactic, and semantic analysis components required little adaptation fo r MUC-5, the most significant change being conversion of the task-specific semantic representation s to object-oriented form. The discourse analysis component was modified to operate on the taskspecific semantic structures, rather than the generic case frames. APPROACH The overall structure and operation of PAKTUS are shown in Figure 1. This is similar to the \"generic system\" described in [1]. Processing proceeds mostly sequentially, with the exception o f the interaction between the syntactic and semantic components at the clause and noun phrase level, and between the lexical analysis and preprocessor. For the MUC5 task, we added some bracketing capabilities to handle the special syntactic phenomena in the financial application domain which might cause problems for the parser or late r extraction processes. These phenomena include company name, currency, temporal expressions, and one percentage expression. For example, \"BRIDGESTONE SPORTS CO .\" , \"BRIDGESTONE SPORTS TAIWAN CO\" , \"UNION PRECISION CASTING CO\" and \"TAG A CO.\" are recognized as company names during the bracketing phase ; \" 20 MILLION NEW TAIWAN DOLLARS\" is bracketed as ((20000000 DOLLA RACURRENCY BASE C^TAIWAN)); and \"75 PCT BY\" is treated as a preposition. The complete sentence parse rate for the MUC5 corpus was significantly improved by the bracketing process, minimizing the need for comple x additions to the grammar. Unlike the generic system, PAKTUS has no text filter or preparser. Full parses are attempted on all sentences, and the first syntactico-semantically successful parse of a sentence is accepted. Parse time is restricted as a linear function of the number of words, however, and parse fragment s are returned, implicitly conjoined, if a full parse cannot be produced in the allotted time. Full parses were achieved for approximately 50 percent of the sentences in the MUC-5 corpus. Useful information was obtained from the fragmentary parses of the other half, however. Based o n comparison of the MUC-5 error measures when fragmentary parses were included and excluded ,", |
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| "paper_id": "M93-1017", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "s. prc. cor n BACKGROUND The PRC Adaptive Knowledge-based Text Understanding System (PAKTUS) was develope d as an Independent Research and Development project at PRC from 1984 through 1992. It includes a core English lexicon and grammar, a concept network, processes for applying these to lexical , syntactic, semantic, and discourse analysis, and tools that support the adaptation of the generi c core to new domains, primarily by acquiring sublanguage and domain-specific lexicon and topic patterns. The lexical, syntactic, and semantic analysis components required little adaptation fo r MUC-5, the most significant change being conversion of the task-specific semantic representation s to object-oriented form. The discourse analysis component was modified to operate on the taskspecific semantic structures, rather than the generic case frames. APPROACH The overall structure and operation of PAKTUS are shown in Figure 1. This is similar to the \"generic system\" described in [1]. Processing proceeds mostly sequentially, with the exception o f the interaction between the syntactic and semantic components at the clause and noun phrase level, and between the lexical analysis and preprocessor. For the MUC5 task, we added some bracketing capabilities to handle the special syntactic phenomena in the financial application domain which might cause problems for the parser or late r extraction processes. These phenomena include company name, currency, temporal expressions, and one percentage expression. For example, \"BRIDGESTONE SPORTS CO .\" , \"BRIDGESTONE SPORTS TAIWAN CO\" , \"UNION PRECISION CASTING CO\" and \"TAG A CO.\" are recognized as company names during the bracketing phase ; \" 20 MILLION NEW TAIWAN DOLLARS\" is bracketed as ((20000000 DOLLA RACURRENCY BASE C^TAIWAN)); and \"75 PCT BY\" is treated as a preposition. The complete sentence parse rate for the MUC5 corpus was significantly improved by the bracketing process, minimizing the need for comple x additions to the grammar. Unlike the generic system, PAKTUS has no text filter or preparser. Full parses are attempted on all sentences, and the first syntactico-semantically successful parse of a sentence is accepted. Parse time is restricted as a linear function of the number of words, however, and parse fragment s are returned, implicitly conjoined, if a full parse cannot be produced in the allotted time. Full parses were achieved for approximately 50 percent of the sentences in the MUC-5 corpus. Useful information was obtained from the fragmentary parses of the other half, however. Based o n comparison of the MUC-5 error measures when fragmentary parses were included and excluded ,", |
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| "section": "Abstract", |
| "sec_num": null |
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| "body_text": [ |
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| "text": "the fragmented parses yielded, on average, about one-third as much correct information as the ful l parses.", |
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| "text": "Another variation on the generic system is that fragment combination and semanti c interpretation are integrated in PAKTUS, and semantic interpretation is divided into two distinc t modules : one that produces a generic representation of the complete sentence, and a subsequen t module that maps this into (possibly several) task-specific representations . In addition, at the lexical analysis phase, as mentioned above, each word was associated with both syntactic an d semantic information . Lexical patterns were developed as an alternative to semantic interpretatio n based on full syntactico-semantic parses . This takes advantage of the information rich lexica l analysis. A pattern matcher uses the results of both the lexical analysis (as in Figure 2a ) and the syntactic analysis (as in Figure 3b ) to extract information . This is invoked when the extractio n based on full parsing fails to yield any data . The patterns are defined as regular expressions tha t are matched against the results of lexical analysis. When a match is found, the corresponding nou n phrases (produced by the full parser) are extracted and the task-specific semantic representation i s constructed from these . PAKTUS has no separate lexical disambiguation module : that function is distributed across al l system modules . Initially, words are assigned all possible meanings available in the lexicon . Senses that are inconsistent with any processing choice are filtered out when that choice i s considered . Figure 2a shows the raw, unprocessed text of the first sentence (Si) of article number 0592 , followed by its lexical analysis. This is the result of applying both the preprocessor and lexica l analysis modules . Each word has one or more senses, represented as a root symbol, which i s generally the concatenation of the English token, the \"^\" character, and the PAKTUS lexica l category (e.g., \"Set^Monotrans\"), or as a simple structure involving a root, lexical category , inflectional mark, and sometimes a conceptual derivation (e .g., the structure \"(Say2^Monotran s L^Monotrans S^ed)\" represents the -ed form of one sense of \"say\") . For each word, all senses in the PAKTUS lexicon are fetched or derived at this time ; disambiguation is generally delayed unti l later phases. Many of the words are unknown to the PAKTUS lexicon; it will make guesses from the context. An example of an ambiguous word is \"concern .\" Figure 2b shows some of the lexicon information for this word in PAKTUS . This includes a list of the 4 PAKTUS primitive words corresponding to the token \"concern\" plus objects containing information for each primitiv e word. These objects are embedded in a semantic network ; they inherit much additiona l information from it.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 750, |
| "end": 759, |
| "text": "Figure 2a", |
| "ref_id": null |
| }, |
| { |
| "start": 796, |
| "end": 805, |
| "text": "Figure 3b", |
| "ref_id": null |
| }, |
| { |
| "start": 1498, |
| "end": 1507, |
| "text": "Figure 2a", |
| "ref_id": null |
| }, |
| { |
| "start": 2421, |
| "end": 2430, |
| "text": "Figure 2b", |
| "ref_id": null |
| } |
| ], |
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| "text": "Sample PAKTUS grammar specifications relevant to S l are shown in Figure 3a , and th e syntactic analysis of this sentence is shown in Figure 3b . This analysis is represented as a configuration of syntactic registers (the main ones are shown in the figure) and register fillers . The grammar fragment of Figure 3a recognized the bound clause in Si (\" . . . it has set up a join t venture. . .\").", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 66, |
| "end": 75, |
| "text": "Figure 3a", |
| "ref_id": null |
| }, |
| { |
| "start": 135, |
| "end": 144, |
| "text": "Figure 3b", |
| "ref_id": null |
| }, |
| { |
| "start": 305, |
| "end": 314, |
| "text": "Figure 3a", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "PROCESSING OF MUC-5 DOCUMENT 059 2", |
| "sec_num": null |
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| "text": "Several semantic frames that apply to S l are shown in Figure 4a , and the generic semanti c analysis of this sentence is shown in Figure 4b . PAKTUS represents the semantic analysis as fiv e case frames : one for the sentence, one for each of the three subordinate clauses (only two of whic h are displayed in the figure), and one for the \"joint venture\" NP . The semantic rules are distribute d in a network of objects like those in Figure 4a .", |
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| "ref_spans": [ |
| { |
| "start": 55, |
| "end": 64, |
| "text": "Figure 4a", |
| "ref_id": null |
| }, |
| { |
| "start": 131, |
| "end": 140, |
| "text": "Figure 4b", |
| "ref_id": null |
| }, |
| { |
| "start": 435, |
| "end": 444, |
| "text": "Figure 4a", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "PROCESSING OF MUC-5 DOCUMENT 059 2", |
| "sec_num": null |
| }, |
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| "text": "Information is organized in several hundred conceptual objects (e.g., CACREATE -the concept for \"set up\" in S l) and case roles (e .g., RARESULT -the thing created) . Information about ho w to map from syntactic registers to case roles may be stored in concept frames, lexical frames, o r role frames, along with semantic constraints on allowable fillers . For example, the RARESULT role of C ACREATE is normally filled by the direct object (DO) register. This information is inherited by RARESULT from the more general R AOBJECT role.", |
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| "section": "PROCESSING OF MUC-5 DOCUMENT 059 2", |
| "sec_num": null |
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| "text": "*** lexical analysis : (((UNKNOWN-WORD L^COMPANY BASE C A UNKNOWN . \"BRIDGESTONE SPORTS CO\") ) ((SAY^INTRANS L^INTRANS S^ED) (SAY^TO-IO L^TO-IO S^ED ) (SAY2^MONOTRANS L A MONOTRANS S A ED) ) ((\"17-NOV-89\" L^TIME-DATE BASE)) ( ", |
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| { |
| "start": 224, |
| "end": 225, |
| "text": "(", |
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| "eq_spans": [], |
| "section": "BRIDGESTONE SPORTS CO . SAID FRIDAY IT HAS SET UP A JOINT VENTURE IN TAIWA N WITH A LOCAL CONCERN AND A JAPANESE TRADING HOUSE TO PRODUCE GOLF CLUB S TO BE SHIPPED TO JAPAN .", |
| "sec_num": null |
| }, |
| { |
| "text": "(CONJOINER24 ANDA CONJ) (R A INSTR18 @F18)) ) (R A PURPOSE3 0 (C ACREATE (R A INSTR35 (F35 (HEAD36 IT^NEUTER)) ) (RA RESULT31 (F31 (HEAD44 (CLUB AGROUP L^GROUP S^S))))) ) (RA PLACE12 (F12 (HEAD13 TAIWAN^NATION)) )", |
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| "section": "IT^NEUTER ) ((HAVE^MONOTRANS L A MONOTRANS S A S) (HAVE2 A INTRANS L A INTRANS S A S ) (HAVE\"INTRANS L^INTRANS S^S) (HAVE^HAVE L^HAVE S^S ) (HAVE1 A MONOTRANS L A MONOTRANS S A S) ) (SET^COLLECTION (SET^MONOTRANS L A MONOTRANS S A ED) SET A MONOTRANS ) (UP^PARTICLE UP A PREP UP^DIRECTION) (A^DET ) (JOINT\\ VENTURE^ACTIVITY ) (IN^PARTICLE IN^PREP) (TAIWAN^NATION ) (WITH^PARTICLE WITH^PREP) (A A DET) (LOCAL^SPACE-REL ) (CONCERN A COPULA CONCERNA MONOTRANS CONCERN^EMOTION CONCERN^BUSINESS ) (AND A CONJ) (A A DET ) ((JAPAN^NATION L^LANGUAGE BASE C^CHAR-OF) (JAPAN^NATION L A ADJ BAS E C^IT-BE-FROM) (JAPAN^NATION L^INHABITANT BASE C A BE-FROM) ) ((UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (TO^PREP TO^PARTICLE) (PRODUCE AMONOTRANS ) ((UNKNOWN-WORD VP BASE C^PRIMITIVE . \"GOLF\" ) (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\") ) ((CLUB^GROUP L^GROUP S^S ) (UNKNOWN-WORD L A INTRANS S A S C A UNKNOWN . \"CLUBS\" ) (UNKNOWN-WORD L A MONOTRANS S\"S C A UNKNOWN . \"CLUBS\") ) (TO A PREP TO^PARTICLE ) (BE A BE BE^INTRANS BE A COPULA ) ((SHIP A BITRANS L A BITRANS S^ED) (SHIPAMONOTRANS L A MONOTRANS S A ED) ) (TOAPREP TO^PARTICLE) (JAPAN^NATION))", |
| "sec_num": null |
| }, |
| { |
| "text": "Figure 4b: Generic Semantic Analysis of S l Figure 5a gives an example of a semantic mapping rule . This consists of a pattern component, which in this case matches the semantic frame of Figure 4b , and a mapping specification (in th e \"slots\" component) . Figure 5b shows the task-specific semantic representation that results fro m applying this mapping to the generic semantic frame. The rule in Figure 5a is a refinement of on e used in the final test MUC-5 system . The final test version recognized the tie up relationship i n this sentence, but extracted only two of the three tie up entities . The new mapping rule bette r illustrates system features without significantly changing the output .", |
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| { |
| "start": 44, |
| "end": 53, |
| "text": "Figure 5a", |
| "ref_id": null |
| }, |
| { |
| "start": 187, |
| "end": 196, |
| "text": "Figure 4b", |
| "ref_id": null |
| }, |
| { |
| "start": 257, |
| "end": 266, |
| "text": "Figure 5b", |
| "ref_id": null |
| }, |
| { |
| "start": 399, |
| "end": 408, |
| "text": "Figure 5a", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "IT^NEUTER ) ((HAVE^MONOTRANS L A MONOTRANS S A S) (HAVE2 A INTRANS L A INTRANS S A S ) (HAVE\"INTRANS L^INTRANS S^S) (HAVE^HAVE L^HAVE S^S ) (HAVE1 A MONOTRANS L A MONOTRANS S A S) ) (SET^COLLECTION (SET^MONOTRANS L A MONOTRANS S A ED) SET A MONOTRANS ) (UP^PARTICLE UP A PREP UP^DIRECTION) (A^DET ) (JOINT\\ VENTURE^ACTIVITY ) (IN^PARTICLE IN^PREP) (TAIWAN^NATION ) (WITH^PARTICLE WITH^PREP) (A A DET) (LOCAL^SPACE-REL ) (CONCERN A COPULA CONCERNA MONOTRANS CONCERN^EMOTION CONCERN^BUSINESS ) (AND A CONJ) (A A DET ) ((JAPAN^NATION L^LANGUAGE BASE C^CHAR-OF) (JAPAN^NATION L A ADJ BAS E C^IT-BE-FROM) (JAPAN^NATION L^INHABITANT BASE C A BE-FROM) ) ((UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (TO^PREP TO^PARTICLE) (PRODUCE AMONOTRANS ) ((UNKNOWN-WORD VP BASE C^PRIMITIVE . \"GOLF\" ) (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\") ) ((CLUB^GROUP L^GROUP S^S ) (UNKNOWN-WORD L A INTRANS S A S C A UNKNOWN . \"CLUBS\" ) (UNKNOWN-WORD L A MONOTRANS S\"S C A UNKNOWN . \"CLUBS\") ) (TO A PREP TO^PARTICLE ) (BE A BE BE^INTRANS BE A COPULA ) ((SHIP A BITRANS L A BITRANS S^ED) (SHIPAMONOTRANS L A MONOTRANS S A ED) ) (TOAPREP TO^PARTICLE) (JAPAN^NATION))", |
| "sec_num": null |
| }, |
| { |
| "text": "The pattern component specifies a semantic structure with a C ATALK concept as its root (th e CAASSERT concept shown in the semantic analysis of S1 is a specialization of C ATALK in PAKTUS), and with RAAGENT and R^PROPOSITION roles. The filler of the RAAGENT role will be bound to the pattern variable RAAGENT250 . The R^PROPOSITION role must be fille d by an instance of a C ABEGIN frame (the CACREATE concept in S 1 is a specialization of C^BEGIN), with a RARESULT that is an instance of C^ATTEMPT (which joint venture is) . If R^COMPANION or R APURPOSE roles are filled in the C^ATTEMPT frame, information i s extracted from them as well (they are optional, which is not indicated in the figure, but is marked i n the actual mapping object).", |
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| "section": "IT^NEUTER ) ((HAVE^MONOTRANS L A MONOTRANS S A S) (HAVE2 A INTRANS L A INTRANS S A S ) (HAVE\"INTRANS L^INTRANS S^S) (HAVE^HAVE L^HAVE S^S ) (HAVE1 A MONOTRANS L A MONOTRANS S A S) ) (SET^COLLECTION (SET^MONOTRANS L A MONOTRANS S A ED) SET A MONOTRANS ) (UP^PARTICLE UP A PREP UP^DIRECTION) (A^DET ) (JOINT\\ VENTURE^ACTIVITY ) (IN^PARTICLE IN^PREP) (TAIWAN^NATION ) (WITH^PARTICLE WITH^PREP) (A A DET) (LOCAL^SPACE-REL ) (CONCERN A COPULA CONCERNA MONOTRANS CONCERN^EMOTION CONCERN^BUSINESS ) (AND A CONJ) (A A DET ) ((JAPAN^NATION L^LANGUAGE BASE C^CHAR-OF) (JAPAN^NATION L A ADJ BAS E C^IT-BE-FROM) (JAPAN^NATION L^INHABITANT BASE C A BE-FROM) ) ((UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (TO^PREP TO^PARTICLE) (PRODUCE AMONOTRANS ) ((UNKNOWN-WORD VP BASE C^PRIMITIVE . \"GOLF\" ) (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\") ) ((CLUB^GROUP L^GROUP S^S ) (UNKNOWN-WORD L A INTRANS S A S C A UNKNOWN . \"CLUBS\" ) (UNKNOWN-WORD L A MONOTRANS S\"S C A UNKNOWN . \"CLUBS\") ) (TO A PREP TO^PARTICLE ) (BE A BE BE^INTRANS BE A COPULA ) ((SHIP A BITRANS L A BITRANS S^ED) (SHIPAMONOTRANS L A MONOTRANS S A ED) ) (TOAPREP TO^PARTICLE) (JAPAN^NATION))", |
| "sec_num": null |
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| { |
| "text": "The mapping portion of the rule specifies how to map the pattern variable bindings to a taskspecific semantic object. This rule says that the result is a tie up relationship with tie up entitie s derived from the bindings of R ACOMPANION248 and RAAGENT250, the joint venture taken as the filler of the RARESULT role of the CABEGIN frame, etc . The type of relationship (existing , former, etc .) is determined by the act-stage function, which computes the type from tense, aspect , and modality registers .", |
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| "section": "IT^NEUTER ) ((HAVE^MONOTRANS L A MONOTRANS S A S) (HAVE2 A INTRANS L A INTRANS S A S ) (HAVE\"INTRANS L^INTRANS S^S) (HAVE^HAVE L^HAVE S^S ) (HAVE1 A MONOTRANS L A MONOTRANS S A S) ) (SET^COLLECTION (SET^MONOTRANS L A MONOTRANS S A ED) SET A MONOTRANS ) (UP^PARTICLE UP A PREP UP^DIRECTION) (A^DET ) (JOINT\\ VENTURE^ACTIVITY ) (IN^PARTICLE IN^PREP) (TAIWAN^NATION ) (WITH^PARTICLE WITH^PREP) (A A DET) (LOCAL^SPACE-REL ) (CONCERN A COPULA CONCERNA MONOTRANS CONCERN^EMOTION CONCERN^BUSINESS ) (AND A CONJ) (A A DET ) ((JAPAN^NATION L^LANGUAGE BASE C^CHAR-OF) (JAPAN^NATION L A ADJ BAS E C^IT-BE-FROM) (JAPAN^NATION L^INHABITANT BASE C A BE-FROM) ) ((UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (TO^PREP TO^PARTICLE) (PRODUCE AMONOTRANS ) ((UNKNOWN-WORD VP BASE C^PRIMITIVE . \"GOLF\" ) (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\") ) ((CLUB^GROUP L^GROUP S^S ) (UNKNOWN-WORD L A INTRANS S A S C A UNKNOWN . \"CLUBS\" ) (UNKNOWN-WORD L A MONOTRANS S\"S C A UNKNOWN . \"CLUBS\") ) (TO A PREP TO^PARTICLE ) (BE A BE BE^INTRANS BE A COPULA ) ((SHIP A BITRANS L A BITRANS S^ED) (SHIPAMONOTRANS L A MONOTRANS S A ED) ) (TOAPREP TO^PARTICLE) (JAPAN^NATION))", |
| "sec_num": null |
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| "text": "(SPEC4 0 (ENTITY-RELATIONSHI P (SPEC42 (ER-STATUS \"CURRENT\") (RELATIONSHIP \"CHILD\" ) (ENTITY2 \"NEW COMPANY\" ) (ENTITY1 \"TAGA CO\" ) (ENTITY1 \"UNION PRECISION CASTING CO\" ) (ENTITY1 \"REMAINDER\" ) (ENTITY1 \"BRIDGESTONE SPORTS\")) ) (TU-ACTIVITY (SPEC46 (SITE (\"KAOHSIUNG\" (APP \"SOUTHERN TAIWAN\")))) ) (OWNERSHI P (SPEC43 (OWNERSHIP-% \"BRIDGESTONE SPORTS\") (OWNERSHIP-% 75 ) (OWNED \"NEW COMPANY\")) ) (OWNERSHI P (SPEC44 (OWNERSHIP-% \"UNION PRECISION CASTING CO\") (OWNERSHIP-% 15 ) (OWNED \"NEW COMPANY\")) ) (OWNERSHI P (SPEC45 (OWNERSHIP-% \"TAGA CO\" ) (OWNED \"NEW COMPANY\")) ) (JOINT-VENTURE (\"NEW COMPANY \"", |
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| "section": "IT^NEUTER ) ((HAVE^MONOTRANS L A MONOTRANS S A S) (HAVE2 A INTRANS L A INTRANS S A S ) (HAVE\"INTRANS L^INTRANS S^S) (HAVE^HAVE L^HAVE S^S ) (HAVE1 A MONOTRANS L A MONOTRANS S A S) ) (SET^COLLECTION (SET^MONOTRANS L A MONOTRANS S A ED) SET A MONOTRANS ) (UP^PARTICLE UP A PREP UP^DIRECTION) (A^DET ) (JOINT\\ VENTURE^ACTIVITY ) (IN^PARTICLE IN^PREP) (TAIWAN^NATION ) (WITH^PARTICLE WITH^PREP) (A A DET) (LOCAL^SPACE-REL ) (CONCERN A COPULA CONCERNA MONOTRANS CONCERN^EMOTION CONCERN^BUSINESS ) (AND A CONJ) (A A DET ) ((JAPAN^NATION L^LANGUAGE BASE C^CHAR-OF) (JAPAN^NATION L A ADJ BAS E C^IT-BE-FROM) (JAPAN^NATION L^INHABITANT BASE C A BE-FROM) ) ((UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (TO^PREP TO^PARTICLE) (PRODUCE AMONOTRANS ) ((UNKNOWN-WORD VP BASE C^PRIMITIVE . \"GOLF\" ) (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\") ) ((CLUB^GROUP L^GROUP S^S ) (UNKNOWN-WORD L A INTRANS S A S C A UNKNOWN . \"CLUBS\" ) (UNKNOWN-WORD L A MONOTRANS S\"S C A UNKNOWN . \"CLUBS\") ) (TO A PREP TO^PARTICLE ) (BE A BE BE^INTRANS BE A COPULA ) ((SHIP A BITRANS L A BITRANS S^ED) (SHIPAMONOTRANS L A MONOTRANS S A ED) ) (TOAPREP TO^PARTICLE) (JAPAN^NATION))", |
| "sec_num": null |
| }, |
| { |
| "text": "(ENTITY-RELATIONSHIP SPEC42)) ) (TU-ENTITY (\"TAGA CO\" (ENTITY-RELATIONSHIP SPEC42)) ) (TU-ENTITY (\"UNION PRECISION CASTING CO\" (LOC \"TAIWAN\" ) (ENTITY-RELATIONSHIP SPEC42)) ) (TU-ENTITY (\"REMAINDER\" (ENTITY-RELATIONSHIP SPEC42)) ) (TU-ENTITY (\"BRIDGESTONE SPORTS\" (ENTITY-RELATIONSHIP SPEC42)) ) (TYPE \"EXISTING\") )", |
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| "section": "IT^NEUTER ) ((HAVE^MONOTRANS L A MONOTRANS S A S) (HAVE2 A INTRANS L A INTRANS S A S ) (HAVE\"INTRANS L^INTRANS S^S) (HAVE^HAVE L^HAVE S^S ) (HAVE1 A MONOTRANS L A MONOTRANS S A S) ) (SET^COLLECTION (SET^MONOTRANS L A MONOTRANS S A ED) SET A MONOTRANS ) (UP^PARTICLE UP A PREP UP^DIRECTION) (A^DET ) (JOINT\\ VENTURE^ACTIVITY ) (IN^PARTICLE IN^PREP) (TAIWAN^NATION ) (WITH^PARTICLE WITH^PREP) (A A DET) (LOCAL^SPACE-REL ) (CONCERN A COPULA CONCERNA MONOTRANS CONCERN^EMOTION CONCERN^BUSINESS ) (AND A CONJ) (A A DET ) ((JAPAN^NATION L^LANGUAGE BASE C^CHAR-OF) (JAPAN^NATION L A ADJ BAS E C^IT-BE-FROM) (JAPAN^NATION L^INHABITANT BASE C A BE-FROM) ) ((UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (TO^PREP TO^PARTICLE) (PRODUCE AMONOTRANS ) ((UNKNOWN-WORD VP BASE C^PRIMITIVE . \"GOLF\" ) (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\") ) ((CLUB^GROUP L^GROUP S^S ) (UNKNOWN-WORD L A INTRANS S A S C A UNKNOWN . \"CLUBS\" ) (UNKNOWN-WORD L A MONOTRANS S\"S C A UNKNOWN . \"CLUBS\") ) (TO A PREP TO^PARTICLE ) (BE A BE BE^INTRANS BE A COPULA ) ((SHIP A BITRANS L A BITRANS S^ED) (SHIPAMONOTRANS L A MONOTRANS S A ED) ) (TOAPREP TO^PARTICLE) (JAPAN^NATION))", |
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| "text": "The complete filled templates for this article are shown in Figure 7 . Some of the information , such as ownership percentages, that was extracted as shown in Figure 6 , does not appear in th e output templates . This is typical of the MUC-5 version of PAKTUS ; we were unable to devote resources sufficient to complete the output generator component (the final processing module i n Figure 1 ), so some information that was extracted was simply ignored by the final process . ", |
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| "start": 60, |
| "end": 68, |
| "text": "Figure 7", |
| "ref_id": "FIGREF2" |
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| { |
| "start": 159, |
| "end": 167, |
| "text": "Figure 6", |
| "ref_id": null |
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| { |
| "start": 384, |
| "end": 392, |
| "text": "Figure 1", |
| "ref_id": "FIGREF0" |
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| "section": "Figure 6 : Task-Specific Semantic Representation of S 4", |
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| "text": "Figure 8 summarizes PRC's scores for MUC-5 . The MUC-5 version of the system was incomplete at the time of the final testing . All modules were functional, but many task-specifi c details were missing . The system was ready for testing on the development corpus only tw o weeks prior to the final test. Performance was improving rapidly -about one point per day . The main limiting factors for PRC were time and availability of people for development. We directed most of our energy toward the basic engineering, such as generating the template formats, which left little time to address the task-specific linguistic requirements . This resulted in severe undergeneration, which accounted for most of the errors (73 percent undergeneration versus 8 3 percent overall error rate) . Figure 9 enumerates our activities and level of effort in connection with the MUC-5 task, a s well as parallel non-Tipster-specific activities in developing our system . Our total developmen t effort in customizing our system for the MUC-5 testing was 2 .1 months, with another month fo r testing, file management, and other non-developmental activities . More than half of the development effort involved non-linguistic engineering of the system fo r MUC-5 requirements, such as the output template format generation. We also needed to convert the extraction components of the system to accommodate the object oriented nature of the MUC-5 templates. This left us with one month of effort for task-specific linguistic development . The specific changes and additions to the PAKTUS knowledge bases for MUC-5 are enumerated i n Figure 10 . In parallel with the MUC-5 activity, we devoted 1 .9 months of effort to generi c development of our system, which may have had some impact on MUC-5 performance .", |
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| "start": 781, |
| "end": 789, |
| "text": "Figure 9", |
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| { |
| "start": 1607, |
| "end": 1616, |
| "text": "Figure 10", |
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| "section": "SYSTEM PERFORMANC E", |
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| "text": "The two areas that could significantly improve performance with modest effort are the definition of task-specific semantic mappings and the output generator. These are highlighted in Figure 11 . Very little was done here, however, due to limited time and resources . Only 147 of the semantic mappings were defined . We estimate that about 1,000 would be needed to map th e generic linguistic data extracted by PAKTUS into the task-specific representations. That would have required about another month of effort . Within the output generator module, which produce s the final output template fills, many functions were incomplete or entirely absent . These are no t difficult to implement, but do require time and effort, which were not available . ", |
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| { |
| "start": 183, |
| "end": 192, |
| "text": "Figure 11", |
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| "section": "Limiting Factors", |
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| "text": "The PAKTUS modules were trained on varying parts of the MUC-5 corpus . The preprocessing and lexical analysis modules were trained from concordances based on about 1,00 0 documents . This included the analysis of corporation names for bracketing . The syntactic and semantic analysis modules were largely unchanged, as noted above . The little tailoring that wa s done was based on a subset of the 86 documents in the dry run, part 1 set. None of this trainin g involved analysis of any complete text, since these modules operate only at the sentence level o r below. The careful analysis required for discourse analysis, including coreference resolution, was performed on only two documents : numbers 0099 and 0102 . These were selected because 0099 contained multiple tie up relationships, and the other contained a single tie up relationship wit h some complex coreference phenomena . This combination seemed to maximize coverage of th e phenomena the system had to deal with, within our very limited resource constraints .", |
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| "section": "System Trainin g", |
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| "text": "Almost all of PAKTUS is generic and can be applied to other applications . All of its processe s are at least partly generic, as illustrated in Figure 12 . They operate on a set of object-oriented knowledge bases, some of which are generic (common English grammar, lexicon, and concep t frames) and some of which are task-specific (input and output templates, semantic mappings, an d topic patterns) . Even within the task-specific knowledge bases, however, the representatio n schemes are generic, and we have tools that facilitate building them.", |
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| { |
| "start": 144, |
| "end": 153, |
| "text": "Figure 12", |
| "ref_id": "FIGREF0" |
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| "section": "Reusability of the Syste m", |
| "sec_num": null |
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| "text": "The primary tasks in applying PAKTUS to a new domain or improving its performance in a n existing domain, are semantic mapping specification, and output generator function developmen t both of which are relatively easy (compared to changing the grammar, for example) .", |
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| "section": "Reusability of the Syste m", |
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| "text": "Two other tasks that must be done, but only once for each new domain, are to specify the inpu t document formats and to identify the output specifications . These are template-driven in PAKTUS . For MUC-5 we converted the BNF specifications supplied by the Government to template format, which is quite simple . We then added a function for each template slot to gather information from our generic discourse data structures . Additional information was included regarding output formats, default fills, etc . These templates are also used by a tool for buildin g semantic mappings.", |
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| "section": "Reusability of the Syste m", |
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| "text": "Generic : Partly Generic: \u2022////VVV/ i Figure 12 : Generic, Reusable Components", |
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| "ref_spans": [ |
| { |
| "start": 38, |
| "end": 47, |
| "text": "Figure 12", |
| "ref_id": "FIGREF0" |
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| ], |
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| "section": "Reusability of the Syste m", |
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| "text": "We confirmed our belief that PAKTUS is robust and adaptable. The more comple x components (syntactic, semantic, and discourse analysis modules) are stable and competen t enough to apply the system to different domains and produce useful results, by adding domainspecific knowledge (lexicon and semantic mappings) . We were once again pleased to learn that it !iiiiiuZ!t!i!!i ii!i\u00bb!,, ", |
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| "section": "Lessons Learned from MUC-5", |
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| "back_matter": [ |
| { |
| "text": "(P-TIE_UP_RELATIONSHIP14 3 (AKO C^TALK ) (R^AGENT ((> R^AGENT250)) ) (R^PROPOSITION (P-TI E_UP_RELATIONSHIP14 4 (AKO ((CON250 IS C^BEGIN)) ) (R^INSTR ((< R^INSTR248)) ) (R^RESULT (P-TIE_UP_RELATIONSHIP15 0 (AKO ((CON249 IS C^ATTEMPT)) ) (R^INSTR @P-TIE_UP_RELATIONSHIP150 ) (R^COMPANION ((R A COMPANION248 IS L^AGENT)) ) (R^PURPOS E (P-TIE_UP_RELATIONSHIP18 6 (AKO ((CON248 IS C^CREATE)) ) (SPEC3 5 (ENTITY-RELATIONSHI P (SPEC37 (RELATIONSHIP \"CHILD\" ) (ENTITY2 \"JOINT VENTURE\" ) (ENTITYI \"LOCAL CONCERN\" ) (ENTITYI \"JAPANESE TRADING HOUSE\" ) (ENTITYI \"BRIDGESTONE SPORTS CO\")) ) (TU-ACTIVIT Y (SPEC38 (SITE \"TAIWAN\" ) (INDUSTRY (SPEC39 (PRODUCT/SERVICE \"GOLF CLUBS\" ) (I-TYPE \"PRODUCTION\")))) ) (JOINT-VENTURE (\"JOINT VENTURE \"(ENTITY-RELATIONSHIP SPEC37)) ) (TU-ENTITY (\"LOCAL CONCERN \"(ENTITY-RELATIONSHIP SPEC37)) ) (TU-ENTITY (\"JAPANESE TRADING HOUSE \" (ENTITY-RELATIONSHIP SPEC37)) ) (TU-ENTITY (\"BRIDGESTONE SPORTS CO \" (ENTITY-RELATIONSHIP SPEC37)) ) (TYPE \"EXISTING\") ) was not necessary to manually analyze much of the corpus in detail . This was done for only two documents for MUC-5 . The full development corpus was used only to customize the preprocessing and lexical analysis components.A task as complex as MUC-5 requires substantial investment in non-linguistic engineerin g before the linguistic capabilities of a system can be applied . This detracts from linguisti c development that might otherwise have been done, and hides much of the linguistic competence o f the system if the engineering is incomplete, as in our case (e .g., correct information was clearly obtained, as in Figure 6 , but not reported due to an incomplete output function) . We recognize the need for such engineering if useful applications are to be achieved, but hope that this process i s standardized quickly so that it does not need to be completely reimplemented for each ne w application.", |
| "cite_spans": [], |
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| "start": 1600, |
| "end": 1608, |
| "text": "Figure 6", |
| "ref_id": null |
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| "eq_spans": [], |
| "section": "annex", |
| "sec_num": null |
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| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "The Generic Information Extraction System", |
| "authors": [ |
| { |
| "first": "J", |
| "middle": [], |
| "last": "Hobbs", |
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| "raw_text": "Hobbs, J . \"The Generic Information Extraction System\", this volume .", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "uris": null, |
| "text": "PAKTUS Modules and Control Flo w", |
| "num": null, |
| "type_str": "figure" |
| }, |
| "FIGREF1": { |
| "uris": null, |
| "text": "Lexical Analysis of the First Sentence of Document Number 059 2 \"CONCERN \" (CONCERN A COPULA CONCERN A MONOTRANS CONCERN^EMOTION CONCERN A BUSINESS ) (CONCERN A COPULA (AKO (L A COPULA) ) (COMPLEMENT (WH-CLAUSE WH-TO-INF NP) ) (CONCEPT (C A BE-ABOUT) ) (ROLES (R A AFFECTED R A PROPOSITION R^FOCUS) ) (R^AFFECTED NIL (_ SUBJECT) (@ \"INFO\") ) (R^FOCUS NIL (_ COMP) (@ (TRUE))) ) (CONCERN A MONOTRANS (AKO ( L AMONOTRANS)) (CONCEPT ( C AMOTIV)) ) (CONCERN^EMOTION (AKO (L A EMOTION)) ) (CONCERN^BUSINESS (AKO (L^BUSINESS)) (TYPE (COUNT LEFT-ADJ-OF-N)) ) Some PAKTUS Lexicon Data Used for S l (C-S-T1E (TO-STATE (E^)) (AKO (ARC) ) (INI T ( ( A .MAIN-VERB .LEX .HAS-FEATURE '(L^VERB ZERO-THAT) ) (* . MOOD_ BOUND)) ) (RULE ( L A S31RULE)) (LABEL (T1)) (FROM-STATE ( C^) ) (NAME (\"cS\\\\tle\")) ) (L^S31RULE (AKO (L A SRULE)) (PRIORITY (0) ) (THEN NIL (ACTIONS (^.PROP_*))) ) Figure 3a: Some PAKTUS Grammar Specifications Used for S l (S (MAIN-VERB55 (SAY A TO-IO L^TO-IO S A ED) ) (SUBJECT5 3 (NP (HEAD5 4 (UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"BRIDGESTONE SPORTS CO\"))) ) (PROP1 1 (T1 (MAIN-VERB51 (ESTABLISH AMONOTRANS L A MONOTRANS S A ED) ) (SUBJECT35 (NP (HEAD36 IT^NEUTER)) ) (DO1 4 (NP (HEAD49 JOINT\\ VENTURE^ACTIVITY) (DET50 A A DET) ) (PROP3 0 (Z A (MAIN-VERB47 PRODUCE A MONOTRANS ) (SUBJECT35 (NP (HEAD36 IT^NEUTER)) ) (DO3 1 (NP (HEAD44 (CLU B A GROUP L^GROUP S A S) ) (PROP3 2 (Z^ (MAIN-VERB39 (SHIP A MONOTRANS L A MONOTRANS S A ED) ) (SUBJECT37 (NP (HEAD38 SOMEONE^SOME)) ) (DO35 (NP (HEAD36 IT^NEUTER)) ) (MODS40 (PP (PREP41 TO A PREP ) (PREP-OBJ33 (NP (HEAD34 JAPAN^NATION)))))) ) (DESC46 (UNKNOWN-WORD L A COMMON BASE C A UNKNOWN . \"GOLF\"))))) ) (MODS1 5 (PP (PREP29 WITH^PREP ) (PREP-OBJ1 8 (NP (HEAD25 CONCERN^BUSINESS) (DET28 A A DET ) (DESC27 LOCAL^SPACE-REL ) (CONJ1 9 (N P (HEAD20 (UNKNOWN-WORD L A COMPANY BASE C A UNKNOWN . \"TRADING HOUSE\") ) (DET23 A A DET ) (DESC22 (JAPAN^NATION L AADJ BASE C^IT-BE-FROM)))))))) ) (MODS1 6 (PP (PREP17 IN^PREP ) (PREP-OBJ12 (NP (HEAD13 TAIWAN^NATION)))))) ) (ADV56 (\"17-NOV-89\" L^TIME-DATE BASE)) ) Figure 3b : Syntactic Analysis of S l (C^CREATE (ROLES (R^AGENT R^RECIPIENT R^INSTR RA RESULT RA PURPOSE R AMATERIAL) ) (AKO (C^BEGIN) ) (R^MATERIAL NIL (_ (PREP-OBJ 'FROM^PREP))) ) (RA RESULT (AKO (RA EFFECT)) ) (RA EFFECT (KINDSOF (RA RESULT R AEVENT)) (AKO (R^OBJECT)) ) (RA OBJEC T (KINDSOF (RA AFFECTED R AEXPERIENCER R^COMPANION RA EFFECT RA PROPOSITIO N RA FOCUS R^PURPOSE R^MATERIAL R^RESISTANCE) ) (_ NIL (DEFAULT DO)) (AKO (PROP-ROLE)) ) Figure 4a : Some PAKTUS Generic Semantic Specifications Used for S l UNKNOWN-WORD L ACOMPANY BASE C^UNKNOWN . \"BRIDGESTONE SPORTS CO\"))) ) (RA PROPOSITION1 1 (C ACREATE (R A INSTR35 (F35 (HEAD36 IT^NEUTER)) ) (RA RESULT1 4 (C AATTEMPT (HEAD49 JOINT\\ VENTURE^ACTIVITY ) (RA INSTR14 @F14 ) (RA COMPANION1 8 (C AACT (HEAD25 CONCERN^BUSINESS ) (CONJ1 9(F1 9 (HEAD20 (UNKNOWN-WORD L^COMPANY BASE C^UNKNOWN . \"TRADING HOUSE\"))) )", |
| "num": null, |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "uris": null, |
| "text": "cont.): PAKTUS Template Fills for the Sample Documen t", |
| "num": null, |
| "type_str": "figure" |
| }, |
| "FIGREF3": { |
| "uris": null, |
| "text": "PRC Score Summary -Breakdown of Development Effor t", |
| "num": null, |
| "type_str": "figure" |
| }, |
| "FIGREF4": { |
| "uris": null, |
| "text": "Additions/Modifications to PAKTUS Knowledge Bases for MUC-5 What Would Improve Performance Quickly ?", |
| "num": null, |
| "type_str": "figure" |
| } |
| } |
| } |
| } |
