| { | |
| "paper_id": "M92-1036", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:13:00.255792Z" | |
| }, | |
| "title": "SRI INTERNATIONAL : DESCRIPTION OF THE FASTUS SYSTE M USED FOR MUC-4", | |
| "authors": [ | |
| { | |
| "first": "Jerry", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Hobbs", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "settlement": "Menlo Park", | |
| "country": "California" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Douglas", | |
| "middle": [], | |
| "last": "Appelt", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "settlement": "Menlo Park", | |
| "country": "California" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mabry", | |
| "middle": [], | |
| "last": "Tyson", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "settlement": "Menlo Park", | |
| "country": "California" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Bear", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "settlement": "Menlo Park", | |
| "country": "California" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Israe", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "settlement": "Menlo Park", | |
| "country": "California" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "INTRODUCTIO N FASTUS is a (slightly permuted) acronym for Finite State Automaton Text Understanding System. It is a system for extracting information from free text in English, and potentially other languages a s well, for entry into a database, and potentially for other applications. It works essentially as a cascaded , nondeterministic finite state automaton. It is an information extraction system, rather than a text understanding system. This distinction is important. In information extraction, only a fraction of the text is relevant. In the case of the MUC-4 terrorist reports, probably only about 10% of the text is relevant. There is a pre-defined, relatively simple , rigid target representation that the information is mapped into. The subtle nuances of meaning and th e writer's goals in writing the text are of no interest. This contrasts with text understanding, where the ai m is to make sense of the entire text, where the target representation must accommodate the full complexitie s of language, and where we want to recognize the nuances of meaning and the writer's goals. The MUC evaluations are information extraction tasks, not text understanding tasks. The TACITU S system that .was used for MUC-3 in 1991 is a text-understanding system [1]. Using it for the information extraction task gave us a high precision, the highest of any of the sites. However, our recall was mediocre , and the system was extremely slow. Our motivation in building the FASTUS system was to have a syste m that was more appropriate to the information extraction task. The inspiration for FASTUS was threefold. First, we were struck by the strong performance that th e group at the University of Massachusetts got out of a fairly simple system [2]. It was clear they were no t doing anything like the depth of preprocessing, syntactic analysis, or pragmatics that was being done by th e systems at SRI, General Electric, or New York University. They were not doing a lot of processing. They were doing the right processing. The second source of inspiration was Pereira's work on finite-state approximations of grammars [3] , especially the speed of the implementation. Speed was the third source. It was simply too embarassing to have to report at the MUC-3 conferenc e that it took TACITUS 36 hours to process 100 messages. FASTUS has brought that time down to 1 1 minutes. The operation of FASTUS is comprised of four steps, described in the next four sections .", | |
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| "paper_id": "M92-1036", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "INTRODUCTIO N FASTUS is a (slightly permuted) acronym for Finite State Automaton Text Understanding System. It is a system for extracting information from free text in English, and potentially other languages a s well, for entry into a database, and potentially for other applications. It works essentially as a cascaded , nondeterministic finite state automaton. It is an information extraction system, rather than a text understanding system. This distinction is important. In information extraction, only a fraction of the text is relevant. In the case of the MUC-4 terrorist reports, probably only about 10% of the text is relevant. There is a pre-defined, relatively simple , rigid target representation that the information is mapped into. The subtle nuances of meaning and th e writer's goals in writing the text are of no interest. This contrasts with text understanding, where the ai m is to make sense of the entire text, where the target representation must accommodate the full complexitie s of language, and where we want to recognize the nuances of meaning and the writer's goals. The MUC evaluations are information extraction tasks, not text understanding tasks. The TACITU S system that .was used for MUC-3 in 1991 is a text-understanding system [1]. Using it for the information extraction task gave us a high precision, the highest of any of the sites. However, our recall was mediocre , and the system was extremely slow. Our motivation in building the FASTUS system was to have a syste m that was more appropriate to the information extraction task. The inspiration for FASTUS was threefold. First, we were struck by the strong performance that th e group at the University of Massachusetts got out of a fairly simple system [2]. It was clear they were no t doing anything like the depth of preprocessing, syntactic analysis, or pragmatics that was being done by th e systems at SRI, General Electric, or New York University. They were not doing a lot of processing. They were doing the right processing. The second source of inspiration was Pereira's work on finite-state approximations of grammars [3] , especially the speed of the implementation. Speed was the third source. It was simply too embarassing to have to report at the MUC-3 conferenc e that it took TACITUS 36 hours to process 100 messages. FASTUS has brought that time down to 1 1 minutes. The operation of FASTUS is comprised of four steps, described in the next four sections .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "In the first pass over a sentence, trigger words are searched for . There is at least one trigger word fo r each pattern of interest that has been defined . Generally, these are the least frequent words required by th e pattern . For example, in the pattern take <HumanTarget> hostage \" hostage\" rather than \" take\" is the trigger word . There are at present 253 trigger words .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In addition, the names of people identified in previous sentences as victims are also treated, for th e remainder of the text, as trigger words . This allows us, for example, to pick up occupations of victims whe n they occur in sentences with no other triggers, as i n Hector Oqueli and Gilda Flores were assassinated yesterday . Gilda Flores was a member of the Democratic Socialist Party (PSD) of Guatemala .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Finally, on this pass, full names are searched for, so that subsequent references to surnames can be linke d to the corresponding full names . Thus, if one sentence refers to \"Ricardo Alfonso Castellar\" but does no t mention his kidnapping, while the next sentence mentions the kidnapping but only uses his surname, we ca n enter Castellar ' s full name into the template .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In Message 48 of TST2, 21 of 30 sentences were triggered in this fashion . 13 of the 21 triggered sentences were relevant . There is very little penalty for passing irrelevant sentences on to further processing since th e system is so fast, especially on irrelevant sentences .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Eight of the nine nontriggered sentences were irrelevant . The one relevant, nontriggered sentence wa s", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "There were seven children, including four of the vice president ' s children, in the home at th e time .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "It does not help to recognize this sentence as relevant as we do not have a pattern that would match it . The missing pattern i s <HumanTarget> be in <PhysicalTarget > which would pick up human targets who were in known physical targets . In order to have this sentenc e triggered, we would have to take the head nouns of known physical targets to be temporary triggers for th e remainder of the text, as we do with named human targets .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "TRIGGERIN G", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The problem of syntactic ambiguity is AI-complete . That is, we will not have systems that reliably parse English sentences correctly until we have encoded much of the real-world knowledge that people bring t o bear in their language comprehension . For example, noun phrases cannot be reliably identified because of th e prepositional phrase attachment problem . However, certain syntactic constructs can be reliably identified . One of these is the noun group, that is, the noun phrase up to the head noun . Another is what we are calling the \" verb group\", that is, the verb together with its auxilliaries and embedded adverbs . Moreover , an analysis that. identifies these elements gives us exactly the units we most need for recognizing patterns o f interest .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Pass Two in FASTUS identifies noun groups, verb groups, and several critical word classes, includin g prepositions, conjunctions, relative pronouns, and the words \"ago\" and \"that\" . Phrases that are subsume d by larger phrases are discarded . Overlapping phrases are rare, but where they occur they are kept . Thi s sometimes compensates for incorrect analysis in Pass Two .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Noun groups are recognized by a 37-state nondeterministic finite state automaton . This encompasses most of the complexity that can occur in English noun groups, including numbers, numerical modifier s like \" approximately\", other quantifiers and determiners, participals in adjectival position, comparative an d superlative adjectives, conjoined adjectives, and arbitrary orderings and conjunctions of prenominal noun s and noun-like adjectives . Thus, among the noun groups recognized are approximately 5 k g more than 30 peasant s the newly elected president, the largest leftist political forc e a government and military reaction Verb groups are recognized by an 18-state nondeterministic finite state machine . They are tagged as Active, Passive, Gerund, and Infinitive . Verbs that are locally ambiguous between active and passive senses , as the verb \"kidnapped\" the the two sentences , Several men kidnapped the mayor today. Several men kidnapped yesterday were released today .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "are tagged as Active/Passive and Pass Three resolves the ambiguity if necessary .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Certain relevant predicate adjectives, such as \"dead\" and \"responsible\", are recognized, as are certai n adverbs, such as \"apparently\" in \"apparently by\" . However, most adverbs and predicate adjectives and man y other classes of words are ignored altogether . Unknown words are ignored unless they occur in a contex t that could indicate they are surnames .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Lexical information is read at compile time, and a hash table associating words with their transitions i n the finite-state machines is constructed . There is a hash table entry for every morphological variant of th e words . Altogether there are 43,000 words in the hash table . During the actual running of the system on the texts, only the state transitions are accessed .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The output of the second pass for the first sentence of Message 48 of TST2 is as follows :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Noun Group : Salvadoran President-elec t Name :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Alfredo Cristian i Verb Group : condemned Noun Group : the terrorist Verb Group : killin g Preposition : of Noun Group :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Attorney Genera l Name :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Roberto Garcia Alvarado Conjunction : and Verb Group : accused Noun Group :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "the Farabundo Marti National Liberation Front (FMLN ) Preposition : of Noun Group : the crime The verb groups \"condemned\" and \"accused\" are labelled \"Active/Passive\" . The word \"killing\" which was incorrectly identified as a verb group is labelled as a Gerund . This mistake is common enough that we hav e implemented patterns to get around it in Pass Three . On Message 48 of TST2, 243 of 252 phrases, or 96 .4%, were correctly recognized . Of the 9 mistakes, 5 were due to nouns being misidentified as verbs or verbs as nouns . 3 were due to a dumb bug in the code for recognizing dates that crept into the system a day before the official run and meant that no explicit dates were recognized except in the header . (This resulted in the loss of 1% in recall in the official run of TST3 . ) One mistake was due to bit rot .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We implemented and considered using a part-of-speech tagger to help in this phase, but there was n o clear improvement and it would have doubled the time the system took to process a message .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PHRASE S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The in put to the third pass of FASTUS is a list of phrases in the order in which they occur . Anythin g that is not included in a phrase in the second pass is ignored in the third pass . The state transitions are driven off the head words in the phrases . In addition, some nonhead words can trigger state transitions . For example, \"bomb blast\" is recognized as a bombing .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We implemented 95 patterns for the 1VIUC-4 application . 14 Apr 8 9 Location :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "El Salvador : San Salvado r Instr:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\"explosives \" Perp :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\"guerrillas \" PTarg:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\"Merino's home\" HTarg :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\" Merino\"", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The incident type is an attack or a bombing, depending on the Device . There was a bug in this patter n that caused the system to miss picking up the explosives as the instrument . In addition, it is disputable whether Merino should be listed as a human target . In the official key template for this message, he is not . But it seems to us that if someone's home is attacked, it is an attack on him .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A certain amount of pseudo-syntax is done while patterns are being recognized . In the first place, the material between the end of the subject noun group and the main verb group must be read over . Since the finite-state mechanism is nondeterministic, the full content can be extracted from the sentenc e The mayor, who was kidnapped yesterday, was found dead today .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "One branch discovers the incident encoded in the relative clause . Another branch marks time through th e relative clause and then discovers the incident in the main clause . These incidents are then merged .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A similar device is used for conjoined verb phrases . one branch will recognize the killing of Garcia and another the fact that Cristiani accused the FMLN . The second sort of \"pseudo-syntax\" that is done while recognizing patterns is attaching genitives, \"of \" complements, and appositives to their heads, and recognizing noun group conjunctions . Thus, i n seven children, including four of the vice-president 's childre n the genitive \"vice-president's\" will be attached to \"children\" . The \"of\" complement will be attached t o \"four\", and since \"including\" is treated as a conjunction, the entire phrase will be recognized as conjoined noun groups .", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In Message 48 of TST2, there were 18 relevant patterns . FASTUS recognized 12 of them completely . Because of bugs in implemented patterns, 3 more patterns were recognized only partially . One implemente d pattern failed completely because of a bug . Specifically, in the sentence A niece of Merino's was injured .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "the genitive marker took the system into a state in which it was not expecting a verb group .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Two more patterns were missing entirely . A rudimentary sort of pronoun resolution is done by FASTUS . If (and only if) a pronoun appears in a Human Target slot, an antecedent is sought . First the noun groups of the current sentence are searche d from left to right, up to four phrases before the pronoun . Then the previous sentences are searched similarl y for an acceptable noun group in a left-to-right fashion, the most recent first . This is continued until th e last. paragraph break, and if nothing is found by then, the system gives up . A noun group is an acceptable antecedent if it is a possible human target and agrees with the pronoun in number . This algorithm worked i n 100% of the relevant cases in the first 200 messages of the development set . However, in its one applicatio n in Message 48 of TST2, it failed . The example is According to the police and Garcia Alvarado 's driver, who escaped unscathed, the attorne y general was traveling with two bodyguards . One of them was injured .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The algorithm incorrectly identifies \" them \" as \"the police\" .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECOGNIZING PATTERN S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "As incidents are found they are merged with other incidents found in the same sentence . Those remainin g at the end of the processing of the sentence are then merged, if possible, with the incidents found in previou s sentences .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "For example, in the first sentence of Message 48 of TST2, the incident . Merging is blocked if the incidents have incompatible types, such as a KIDNAPPING and a BOMBING . I t is also blocked if they have incompatible dates or locations . There are fairly elaborate rules for merging the noun groups that appear in the Perpetrator, Physica l Target, and Human Target slots . A name can be merged with a precise description, as \"Garcia\" with \"attorney general\", provided the description is consistent with the other descriptions for that name . A precise description can be merged with a vague description, such as \"person\", with the precise description as the result . Two precise descriptions can be merged if they are semantically compatible . The description s \" priest \" and \"Jesuit \" are compatible, while \" priest \" and \"peasant \" are not . When precise descriptions ar e merged, the longest string is taken as the result . If merging is impossible, both noun groups are listed in th e slot .", | |
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| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
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| { | |
| "text": "We experimented with a further heuristic for when to merge incidents . If the incidents include name d human targets, we do not merge them unless there is an overlap in the names . This heuristic results in abou t a 1% increase in recall . In Message 48 of TST2, the heuristic prevents the Bombing of Garcia Alvarado' s car from being merged with the Bombing of Merino 's home .", | |
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| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
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| { | |
| "text": "There were 13 merges altogether in processing Message 48 of TST2 . Of these, 11 were valid . One of the two bad merges was particularly unfortunate . The phrase . . . Garcia Alvarado's driver, who escaped unscathed, . . . correctly generated an attack incident with no injury to the human target, the driver :", | |
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| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Incident : ATTAC K Perp : PTarg : HTarg :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\"Garcia Alvarado's driver\" HEffect :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "No Injury", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This was merged with the attack on Merino 's home Incident : BOMBIN G Perp :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\" guerrillas\" PTarg : \" Merino's home\" HTarg :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\" Merino \" HEffect :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "to yield the combined incident Incident : BOMBIN G Perp :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\"guerrillas \" PTarg : \" Merino ' s home \" HTarg :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\"Merino\" : \" Garcia Alvarado 's driver \" HEffect :", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "No Injury That is, it was assumed that Merino was the driver . The reason for this mistake was that while a certai n amount of consistency checking is done before merging victims, and while the system knows that drivers an d vice presidents-elect are disjoint sets, the fact that Merino was the vice president-elect was recorded only i n a table of titles, and consistency checking did not consult that table .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "MERGING INCIDENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "FASTUS made 25 errors on Message 48 of TST2, where a wrong answer, a missing answer, and a spurious answer are all counted as errors . (There is in principle no limit to the number of possible errors , since arbitrarily many spurious entries could be given . However, practically the number of possible error s is around 80 . If no entries are made in the templates, that counts as 55 errors . If all the entries are made and are correct, but combined into a single template, that counts as 48 errors-the 24 missing entries in th e smaller template and the 24 spurious entries in the larger . )", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "ERROR ANALYSIS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The sources of the errors are as follows : Because of the missing patterns, we failed to find the children and the bodyguards as human targets . The bad merges resulted in the driver being put into the wrong template . The armored car was found as a physical target in the attack against Garcia Alvarado, but armored cars are viewed as military, and military targets are filtered out just before the templates are generated . The disputable answer is Merino as a huma n target in the bombing of his home .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "ERROR ANALYSIS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We do not know to what extent this pattern of causes of errors is representative of the performance o f the system on the corpus as a whole .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "ERROR ANALYSIS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "If we had had one more month to work on the MUC-4 task, we would have spent the first week developin g a rudimentary pattern specification language . We believe that with about two months work we could develo p a langauge that would allow a novice user to he able to begin to specify patterns in a new domain withi n hours of being introduced to the system . The pattern specification language would allow the user to defin e structures, to specify patterns in regular expressions interrupted by assignments to fields of the structures , and to define a sort hierarchy to control the merging of structures .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "FUTURE DIRECTION S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We would also like to apply the system to a new domain . Our experience with the MUC-4 task leads u s to believe we could achieve reasonable performance on the new domain within two months .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "FUTURE DIRECTION S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Finally, it. would be interesting to try to convert FASTUS to a new language . There is not much linguisti c knowledge built into the system . What there is probably amounted to no more than two weeks coding . Fo r this reason, we believe it would require no more than one or two months to convert the system to another language . This is true even for a language as seemingly dissimilar to English as Japanese . In fact, ou r approach to recognizing phrases was inspired in part by the bunsetsu analysis of Japanese .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "FUTURE DIRECTION S", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "The research was funded by the Defense Advanced Research Projects Agency under Office of Nava l Research contracts N00014-90-C-0220, and by an internal research and development grant from SRI International .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "ACKNOWLEDGEMENT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The advantages of the FASTUS system are as follows :\u2022 It is conceptually simple . It is a cascaded finite-state automaton .\u2022 The basic system is relatively small, although the dictionary and other lists are potentially very large .\u2022 It is effective . Only General Electric 's system performed significantly better than FASTUS, and it ha s been under development for a number of years .\u2022 It has very fast run time . The average time for analyzing one message is less than 7 seconds .\u2022 In part because of the fast run time, it has a very fast development time . This is also true because th e system provides a very direct link between the texts being analyzed and the data being extracted .FASTUS is not a text understanding system . It is an information extraction system . But for information extraction tasks, it is perhaps the most convenient and most effective system that has been developed .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| } | |
| ], | |
| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Interpretation as Abduction", | |
| "authors": [ | |
| { | |
| "first": "Jerry", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Hobbs", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Stickel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Mark", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Douglas", | |
| "middle": [], | |
| "last": "Appelt", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Paul", | |
| "middle": [], | |
| "last": "Martin", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1990, | |
| "venue": "SRI International Artificial Intelligence Center Technical Note", | |
| "volume": "499", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Hobbs, Jerry R ., Stickel, Mark, Appelt, Douglas, and Martin, Paul, \" Interpretation as Abduction\" , SRI International Artificial Intelligence Center Technical Note 499, December 1990 .", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Description o f the CIRCUS System as Used for MUC-3", | |
| "authors": [ | |
| { | |
| "first": "Wendy", | |
| "middle": [], | |
| "last": "Lehnert", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Claire", | |
| "middle": [], | |
| "last": "Cardie", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Fisher", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ellen", | |
| "middle": [], | |
| "last": "Riloff", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Robert", | |
| "middle": [], | |
| "last": "Williams", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1991, | |
| "venue": "Proceedings, Third Message Understanding Conference (MUC-3)", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "223--233", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Lehnert, Wendy, Claire Cardie, David Fisher, Ellen Riloff, and Robert Williams, 1991 . \"Description o f the CIRCUS System as Used for MUC-3\", Proceedings, Third Message Understanding Conference (MUC-3) , San Diego, California, pp . 223-233 .", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "Finite-State Approximations of Grammars", | |
| "authors": [ | |
| { | |
| "first": "Fernando", | |
| "middle": [], | |
| "last": "Pereira", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1990, | |
| "venue": "Proceedings, DARPA Speech an d Natural Language Workshop", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "20--25", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Pereira, Fernando, 1990 . \"Finite-State Approximations of Grammars\", Proceedings, DARPA Speech an d Natural Language Workshop, Hidden Valley, Pennsylvania, pp . 20-25 .", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF0": { | |
| "text": "The system is implemented in CommonLisp and runs on both Suns and Symbolics machines .", | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null | |
| }, | |
| "FIGREF1": { | |
| "text": "There are patterns to accomplish this . Two of them are as follows : Subject {Preposition NounGroup}* VerbGrou p Subject Relpro {NounGroup Other}* VerbGroup {NounGroup I Other}* VerbGroup The first of these patterns reads over prepositional phrases . The second over relative clauses . The ver b group at the end of these patterns takes the subject noun group as its subject . There is another pattern fo r capturing the content encoded in relative clauses : Subject Relpro {NounGroup Other}* VerbGrou p", | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null | |
| }, | |
| "FIGREF2": { | |
| "text": "the clause Salvadoran President-elect Alfredo Cristiani . . . accused the Farabundo Marti National Liberatio n Front (FMLN ) These two incidents are merged, by merging the KILLING and the INCIDENT into a KILLING, and b y taking the union of the other slots .", | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null | |
| }, | |
| "FIGREF3": { | |
| "text": "", | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null | |
| }, | |
| "TABREF0": { | |
| "num": null, | |
| "content": "<table><tr><td colspan=\"2\">bomb was placed by <Perp> on <PhysicalTarget ></td></tr><tr><td colspan=\"2\"><Perp> attacked <Huma.nTarget> ' s <PhysicalTarget> with <Device></td></tr><tr><td colspan=\"2\"><HumanTarget> was injure d</td></tr><tr><td colspan=\"2\"><HumanTarget> 's body</td></tr><tr><td colspan=\"2\">matches the pattern</td></tr><tr><td colspan=\"2\"><Perp> attacked <HumanTarget> ' s <PhysicalTarget> in <Location ></td></tr><tr><td colspan=\"2\"><Date> with <Device ></td></tr><tr><td colspan=\"2\">This causes the following incident to be constructed .</td></tr><tr><td>Incident :</td><td>ATTACK/BOMBIN G</td></tr><tr><td>Date :</td><td/></tr><tr><td/><td>Among the patterns are the following ones tha t</td></tr><tr><td colspan=\"2\">are relevant to Message 48 of TST2 :</td></tr><tr><td colspan=\"2\">killing of <HumanTarget ></td></tr><tr><td colspan=\"2\"><GovtOfficial> accused <PerpOrg></td></tr></table>", | |
| "html": null, | |
| "text": "As patterns are recognized, incident structures are built up . For example, the sentence Guerrillas attacked Merino's home in San Salvador 5 days ago with explosives .", | |
| "type_str": "table" | |
| } | |
| } | |
| } | |
| } |