{
"paper_id": "M92-1014",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T03:13:19.070703Z"
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"title": "CRL/NMSU and Brandeis MucBr'uce: MUC-4 Test Results and Analysi s",
"authors": [
{
"first": "Jim",
"middle": [],
"last": "Cowie",
"suffix": "",
"affiliation": {
"laboratory": "Computing Research Laboratory",
"institution": "New Mexico State University",
"location": {}
},
"email": ""
},
{
"first": "Louise",
"middle": [],
"last": "Guthrie",
"suffix": "",
"affiliation": {
"laboratory": "Computing Research Laboratory",
"institution": "New Mexico State University",
"location": {}
},
"email": ""
},
{
"first": "Yorick",
"middle": [],
"last": "Wilks",
"suffix": "",
"affiliation": {
"laboratory": "Computing Research Laboratory",
"institution": "New Mexico State University",
"location": {}
},
"email": ""
},
{
"first": "James",
"middle": [],
"last": "Pustejovsky",
"suffix": "",
"affiliation": {
"laboratory": "Brandeis University INTRODUCTIO N The Computing Research Laboratory (New Mexico State University)",
"institution": "",
"location": {}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "and the Computer Science Departmen t (Brandeis University) are collaborating on the development of a system (DIDEROT) to perform data extraction for the Tipster project. This system is still far from fully developed, but as many of the techniques bein g used are domain-and in many cases language-independent, we have assembled them in a preliminar y manner to produce a prototype system (MucBruce l), which handles the MUC-4 texts. The overall system architecture is shown in Figure 1. The development of the software and data used for MucBruce has been carried out over a three mont h period beginning at the end of February, 1992. The present version of the system relies extensively on statistically-based measures of relevance made both at the text and the paragraph level. Texts are tagge d for a variety of features by a pipeline of processes. The marked texts and the paragraph relevancy informatio n are used to allow a scan around keywords for appropriate slot filling strings. The system has been augmented since the dry-run with a parser which processes sentences which contain a word with an associated Generativ e Lexical Semantic (GLS) definition. This component was added by Brandeis late in the development process and has access to approximately 20 lexical definitions. Our results reflect the extremely simplistic approach to identifying the slot fills in a text. We feel confident , however, that an expansion of the coverage of our GLS entries and the addition of further constraints to prevent template overgeneration will produce significant improvements. We have created a set of tagging and statistical techniques which will apply to any text type, given appropriate training data .",
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"abstract": [
{
"text": "and the Computer Science Departmen t (Brandeis University) are collaborating on the development of a system (DIDEROT) to perform data extraction for the Tipster project. This system is still far from fully developed, but as many of the techniques bein g used are domain-and in many cases language-independent, we have assembled them in a preliminar y manner to produce a prototype system (MucBruce l), which handles the MUC-4 texts. The overall system architecture is shown in Figure 1. The development of the software and data used for MucBruce has been carried out over a three mont h period beginning at the end of February, 1992. The present version of the system relies extensively on statistically-based measures of relevance made both at the text and the paragraph level. Texts are tagge d for a variety of features by a pipeline of processes. The marked texts and the paragraph relevancy informatio n are used to allow a scan around keywords for appropriate slot filling strings. The system has been augmented since the dry-run with a parser which processes sentences which contain a word with an associated Generativ e Lexical Semantic (GLS) definition. This component was added by Brandeis late in the development process and has access to approximately 20 lexical definitions. Our results reflect the extremely simplistic approach to identifying the slot fills in a text. We feel confident , however, that an expansion of the coverage of our GLS entries and the addition of further constraints to prevent template overgeneration will produce significant improvements. We have created a set of tagging and statistical techniques which will apply to any text type, given appropriate training data .",
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"section": "Abstract",
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{
"text": "The system consists of three front-end components all of which are C or Lex programs :",
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"section": "SYSTEM FEATURE S",
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{
"text": "\u2022 A text relevancy marke r",
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"section": "SYSTEM FEATURE S",
"sec_num": null
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"text": "\u2022 A paragraph relevancy marker",
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"section": "SYSTEM FEATURE S",
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"text": "\u2022 A text tagging pipeline and two MUC specific Prolog programs :",
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"section": "SYSTEM FEATURE S",
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"text": "\u2022 A template constructor \u2022 A template formatte r ' We seem to have adopted a philosophical stance for our system nomenclature, and this particular Australian philosophe r seemed to embody some of the ad hoc notions which, at the moment, glue our system together.",
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"section": "SYSTEM FEATURE S",
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"text": "Input file : TST2-MUC4-0007, f 4-4.",
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"section": "Tagge r",
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"text": "Overview} Quit ) One of our principal intentions is to automate as much as possible all the processes associated with th e creation of a text extraction system . Our statistical techniques for relevant text recognition use word list s which are automatically derived from training data . Our text tagger uses proper name information derive d from the key templates and other taggers for human names and dates are largely domain independent . We intend to derive the entire core lexicon for the system from Machine Readable Dictionaries and then to tun e it against appropriate corpora .",
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"section": "Start",
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"text": "Our results are shown in tables 1 and 2 . The results for test 4 are much poorer then those for test 3 . We have not established any specific causes for this difference . For most of the individual slots we see some improvement in recall and a greater improvement in precision over the results of the dry run test . Th e MucBruce system is not parameterized in any way to affect recall or precision . To change these we woul d require modifying the parameters given to the text statistics programs . For MUC-4 we tried to improve precision at the expense of some recall . It is extremely difficult to measure the accuracy of the templat e predicting programs, as their performance can be easily masked by errors occurring in the template producing sections of the system . We need to run separate tests of these components to establish the exact relationshi p on performance of the text statistics, text marking and template producing components . We have not yet , however, had time to carry out these tests on the new MUC-4 data .",
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"section": "OFFICIAL RESULT S",
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"text": "Approximately ten people have worked at one time or another on the MUC-4 system over the last thre e months . They were all, however, also working on other projects over this period . A rough estimate of the time involved would be six person-months . The major areas of work were in developing and refinin g Our limiting factor was definitely time . In the last month we generalized the lexical entries in ou r tagging file . This meant our system was often likely to recognize partial strings as being appropriate filler s (e .g . GUERILLAS) . We intended to avoid this problem by incorporating the BBN part of speech tagge r (POST) into our MUC-4 system and to write code to glue together noun phrases occurring around our ne w general tags . All this code was written and tested just before the MUC-4 final test, but we were unable t o incorporate it in time .",
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"section": "EFFORT SPEN T",
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"text": "The training texts were used to generate our statistical information and word lists . The methods use d are automatic and require only the setting of thresholds for word selection .",
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"text": "The system has improved its performance slightly since the dry run test . Many of our changes in isolation are detrimental and require the addition of other techniques to establish their usefulness . 76 144 26 11 10 1 11 41 22 6 7 inc-loc 76 140 1 41 5 0 9 28 15 6 6 inc-type 76 144 37 10 0 0 0 55 29 67 8 inc-stage 76 144 45 0 2 0 0 59 31 67 2 2 inc-instr-id 32 54 15 0 1 0 0 47 28 7 0 inc-instr-type 52 54 15 1 1 0 0 30 29 68 ",
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"text": "76 144 26 11 10 1 11 41 22 6 7 inc-loc 76 140 1 41 5 0 9 28 15 6 6 inc-type 76 144 37 10 0 0 0 55 29 67 8 inc-stage 76 144 45 0 2 0 0 59 31 67 2 2 inc-instr-id 32 54 15 0 1 0 0 47 28 7 0 inc-instr-type 52 54 15 1 1 0 0 30 29 68",
"ref_id": "TABREF2"
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"section": "EFFORT SPEN T",
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"text": "The basic system is essentially domain-independent and around 80% of it should be directly usable in othe r applications . The module which needs the greatest amount of work is the template creator . Much of this will be replaced as we develop our system for Tipster . It would have been nice to see the effect of adding the part of speech tagger and the noun phrase recognizer to the system . The test deadlines and the availability of the MUC-3 corpus have proved extremely useful to our researc h efforts, both encouraging us to get a robust working system together and to look critically at its performance .",
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"section": "CONCLUSION S",
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"FIGREF0": {
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"type_str": "figure",
"text": "MucBruce -System Overvie w",
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"TABREF2": {
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"text": "TEST 3 Summary Scores the statistical techniques, designing and developing the tagging software and implementing a system whic h could use our current incomplete set of components . Work also went into designing and implementing an appropriate form for the Generative Lexical Semantic entries ."
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"text": "TEST 4 Summary Scores"
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