| { | |
| "paper_id": "M91-1017", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:15:35.544440Z" | |
| }, | |
| "title": "UNISYS : MUC-3 TEST RESULTS AND ANALYSI S", | |
| "authors": [ | |
| { | |
| "first": "Carl", | |
| "middle": [], | |
| "last": "Weir", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Unisys Center for Advanced Information Technolog y Paoli", | |
| "location": { | |
| "country": "Pennsylvani" | |
| } | |
| }, | |
| "email": "weir@prc.unisys.com" | |
| }, | |
| { | |
| "first": "Robin", | |
| "middle": [], | |
| "last": "Mcentire", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Unisys Center for Advanced Information Technolog y Paoli", | |
| "location": { | |
| "country": "Pennsylvani" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Barry", | |
| "middle": [], | |
| "last": "Silk", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Unisys Center for Advanced Information Technolog y Paoli", | |
| "location": { | |
| "country": "Pennsylvani" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tim", | |
| "middle": [], | |
| "last": "Finin", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Unisys Center for Advanced Information Technolog y Paoli", | |
| "location": { | |
| "country": "Pennsylvani" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "The Unisys MUC-3 system is based on a three-tiered approach to text processing in which a novel an d quite powerful knowledge-based form of information retrieval plays a central role. The main components of this approach are as follows : A Keyword-Based Information Retrieval Component. This component predicts the occurrence of types of events in texts based on the presenc e of key words and phrases. A Knowledge-Based Information Retrieval Component. This component, called KBIRD in the Unisys MUC-3 system, performs the followin g tasks :", | |
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| "paper_id": "M91-1017", | |
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| "abstract": [ | |
| { | |
| "text": "The Unisys MUC-3 system is based on a three-tiered approach to text processing in which a novel an d quite powerful knowledge-based form of information retrieval plays a central role. The main components of this approach are as follows : A Keyword-Based Information Retrieval Component. This component predicts the occurrence of types of events in texts based on the presenc e of key words and phrases. A Knowledge-Based Information Retrieval Component. This component, called KBIRD in the Unisys MUC-3 system, performs the followin g tasks :", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "\u2022 Based on the co-occurrence of the predictions made by the keyword-based analysi s component and expressions and concepts discovered in a given text, it predicts th e likely occurrence of additional event types . \u2022 It locates instances of predicted event types in texts .", | |
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| "text": "\u2022 It identifies possible slot values for located instances of events .", | |
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| "sec_num": null | |
| }, | |
| { | |
| "text": "[A Linguistic Analysis Component . ]", | |
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| "text": "Although a natural language processing component was included in the design of th e Unisys MUC-3 system as a third level of text analysis, not enough time was availabl e during the MUC-3 development cycle both to develop a knowledge-based information retrieval component and to port the Unisys Pundit text-processing system to the MUC -3 terrorist domain . A decision was made to focus on developing the knowledge-base d information retrieval component and postpone the integration of Pundit until MUC-4 .", | |
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| "text": "A Template Generation Component . An application-specific Prolog program was written to merge templates describing the same event, and to select the most likely slot values for templates in cases where multiple slot values were proposed .", | |
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| "text": "The Unisys MUC-3 development effort was comprised of two full-time Unisys staff members and on e government employee on industrial rotation . A total of 2650 person-hours were put into the project, 80 0 of which were contributed by the government employee . The effort was partially supported by a DARP A grant, which covered approximately 30% of the development cost .' The bulk of the effort involved the development of the KBIRD system and its MUC-3 rule base . These two tasks took approximately the same amount of time, and in total comprised roughly 85% of the effort .", | |
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| { | |
| "text": "ACT ", | |
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| "eq_spans": [], | |
| "section": "POS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "COR PAR INC ICR IPA SPU MIS NON ABC FRB OVO PA", | |
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| "eq_spans": [], | |
| "section": "POS", | |
| "sec_num": null | |
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| { | |
| "text": "The scores reported for the Unisys MUC-3 system are shown in Figure 1 . The low ACT and high MI S scores reported for the template id slot indicate that event detection was a problem . 2 Poor event detectio n performance explains the relatively low recall scores reported for all but the MATCHED ONLY summary measurement . The MATCHED ONLY recall score is a measure of performance in which spurious (fals e positive) and missing (false negative) templates are not factored in . The extremely low SPU score reporte d for the template id slot suggests that further training of the rule base to improve event detection will no t come at the expense of lower precision scores . In Figure 2 , the performance of the Unisys system wit h respect to other MUC-3 systems is indicated in two scatter plots .", | |
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| { | |
| "start": 61, | |
| "end": 69, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 677, | |
| "end": 685, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "TEST RESULT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Since template slot-filling algorithms are triggered by the detection of an event, poor event detectio n performance has a direct negative impact on slot-filling performance . The recall scores for the Unisys MUC-3 system reflect this fact . However, for five slots precision scores are also low . These low precisio n scores are not a consequence of poor event detection, but result instead from a combination of poorl y trained inference rules used to extract the sort of information expressed in the pertinent slots, and bug s in the template generation routines that gather and merge correctly detected information into templat e structures .", | |
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| "section": "TEST RESULT S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Contrary to what the low recall scores that have been reported suggest, the Unisys MUC-3 system ca n perform well at predicting events . The keyword-based prediction of event types is very robust ; the databas e used during this stage of processing was derived from the full 1300 message DEV corpus . Moreover, whe n the rules used by KBIRD are properly trained, they do a very good job of locating instances of th e events predicted by keyword analysis . Unfortunately, the KBIRD locator rules used to detect instance s of events were trained on a relatively small set of messages-the 200 NOSC DEV and TST1 messages . general event detection rules in place had recall scores ranging in the high 30's and low 40's for all th e summary measures . A tactical mistake was made in attempting to replace this general rule base with a larger, more context-sensitive one, since there was not enough time to allow the larger rule base to b e properly trained . In the evaluation, generating spurious templates tended to have much less of an impac t on scores than failing to generate templates at all . In future evaluations, we will investigate the use o f different locator rule sets as a settable system parameter .", | |
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| "section": "ANALYSIS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Rule training was hindered during the MUC-3 development cycle by the need to concurrently build the component that would be using the rules . In addition to this development problem, technical difficulties in KBIRD's design began to appear once the number of rules had grown to a realistic size . These technical problems resulted in slow message processing speeds, which further complicated the rule training process . The following three key problems were identified : Heavy use of forward-chaining .", | |
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| "section": "ANALYSIS", | |
| "sec_num": null | |
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| { | |
| "text": "There is currently too much reliance on forward-chaining in the KBIRD system . Many KBIRD reasoning tasks could be more efficiently achieved in a backward-chaining fashion . Expensive TMS system . KBIRD was built on top of a very general inferencing mechanism with an expensiv e TMS system . KBIRD's needs for truth maintenance could be accomodated using a muc h simpler TMS component .", | |
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| "section": "ANALYSIS", | |
| "sec_num": null | |
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| { | |
| "text": "Inability to focus search . In KBIRD, it is currently not possible to focus search on a specific region of text . Th e mechanism used to satisfy a rule looks for all chart elements (concepts, words, phrases , and so forth) that match constituent expressions in the antecedent of a rule . If the KBIR D rule specifies that an element of a certain type must be in the same sentence as some other element, it would be more efficient to limit the search space to just those chart element s that fall within the span of the sentence . However, KBIRD's algorithm currently searche s through chart elements indexed to locations anywhere in the text for suitable candidates .", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "ANALYSIS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "'Work on this project was partially supported by Darpa under contract MDA-903-89-C-0041 .", | |
| "cite_spans": [], | |
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| "text": "The template id slot is scored differently from other slots-the values reported for this slot are a measure of even t detection performance (it doesn ' t make sense to report system performance in generating template ids, since the order i n which templates are generated is not relevant in this task)[2] .", | |
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| "back_matter": [ | |
| { | |
| "text": "The time constraints imposed in MUC-3 made it impossible to fully develop the Unisys MUC-3 system' s knowledge-based information retrieval component, KBIRD, before the evaluation deadline . Consequently , it is not possible at this time to establish the capabilities of the three-tiered approach realized in th e system . The system's scores indicate, however, that although the rules for locating instances of event s were inadequately trained, its performance at identifying slot values once an instance has been found i s quite good .Future work on the system will solve the technical problems that have been observed . This will b e achieved by performing the following tasks :\u2022 The overall system flow will be restructured to allow backward-chaining to handle more of the processing load .\u2022 The current forward-chaining mechanism will be reimplemented so that it is specifically geared t o the processing tasks envisoned for KBIRD .\u2022 Subject to an appropriate funding source, the KBIRD locator rules used to detect instances o f predicted event types will be properly trained .In addition to solving the technical problems that have arisen in the system's KBIRD component, a major effort will be made to incorporate the Unisys Pundit NLP system into the MUC-3 system .", | |
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| "section": "CONCLUDING REMARK S", | |
| "sec_num": null | |
| } | |
| ], | |
| "bib_entries": { | |
| "BIBREF0": { | |
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| "urls": [], | |
| "raw_text": "Tim Finin, Robin McEntire, Carl Weir, and Barry Silk . A three-tiered approach to natural languag e text retrieval . In Proceedings of the AAAI workshop on Natural Language Text Retrieval, Los Angeles , July 1991 .", | |
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| }, | |
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| "ref_id": "b1", | |
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| "raw_text": "Carl Weir, Tim Finin, Barry Silk, Marcia Linebarger, and Robin McEntire . Knowledge-based strategie s for robust text-understanding . The Eighth Annual Intelligence Community AI/Advance Computing Symposium, March 1991 .", | |
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| "text": "Consequently, even though the keyword-based analysis phase may have correctly predicted the likel y occurrence of a given event type, KBIRD may not have been able to locate an instance of the predicte d event type . Thus, KBIRD ' s locator rules had a negating influence on the performance of the keywordbased analysis phase . Prior to the final MUC-3 test, versions of the Unisys system with fewer, The scatter plot on the left indicates the relative performance of the Unisys MUC-3 system withou t taking into consideration false negative and false positive hits (the MATCHED-ONLY score) . The scatter plo t on the right indicates the relative performance of the Unisys MUC-3 system when taking into consideratio n both false negative and false positive hits (the ALL-TEMPLATES score) ." | |
| } | |
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