| { |
| "paper_id": "M91-1012", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T03:15:31.645812Z" |
| }, |
| "title": "MCDONNELL DOUGLAS ELECTRONIC SYSTEMS COMPANY : MUC-3 Test Results and Analysi s", |
| "authors": [ |
| { |
| "first": "David", |
| "middle": [], |
| "last": "De Hilster", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "Advanced Computing Technologies Lab McDonnell Douglas Electronics Systems Compan y", |
| "institution": "East Saint Andrew Plac e Santa Ana", |
| "location": { |
| "postCode": "1801, 92705-652 0", |
| "region": "California" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Amnon", |
| "middle": [], |
| "last": "Meyers", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "Advanced Computing Technologies Lab McDonnell Douglas Electronics Systems Compan y", |
| "institution": "East Saint Andrew Plac e Santa Ana", |
| "location": { |
| "postCode": "1801, 92705-652 0", |
| "region": "California" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "INLET scored 25% recall and 35% precision after interactive correction. Noninteractive scores were 22% and 32%, respectively. Relative to other sites, we ranked 7th i n recall and 9th in precision. Because we are in transition from one system (VOX) to another (INLET), our score s reflect the performance of a very new and incomplete analyzer, as well as a small vocabulary and knowledge base. To be able to participate in MUC3, we implemented only a skimming capability, rather than a full-fledged syntax-driven language analyzer. Considering the statu s of our project, we feel INLET's performance is highly commendable. We ourselves were surprised by the success of skimming alone in performance on the MUC3 task, especiall y considering the preliminary nature of our work in this area .", |
| "pdf_parse": { |
| "paper_id": "M91-1012", |
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| "abstract": [ |
| { |
| "text": "INLET scored 25% recall and 35% precision after interactive correction. Noninteractive scores were 22% and 32%, respectively. Relative to other sites, we ranked 7th i n recall and 9th in precision. Because we are in transition from one system (VOX) to another (INLET), our score s reflect the performance of a very new and incomplete analyzer, as well as a small vocabulary and knowledge base. To be able to participate in MUC3, we implemented only a skimming capability, rather than a full-fledged syntax-driven language analyzer. Considering the statu s of our project, we feel INLET's performance is highly commendable. We ourselves were surprised by the success of skimming alone in performance on the MUC3 task, especiall y considering the preliminary nature of our work in this area .", |
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| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "Like many other developers of NLP systems, we have had no reason, up to now, to parameterize the trade-off between recall and precision . At various points in the past fe w months, we had tested heuristics that increased recall while reducing precision .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "TEST SETTING S", |
| "sec_num": null |
| }, |
| { |
| "text": "Participation in MUC3 has led us to examine such a parameter more seriously . In particular, a confidence metric, whereby the system assesses its own confidence i n understanding a text, seems to be a useful component of NLP system development . We can attac h such confidence factors to every action that the system takes in processing text, in extractin g information from text, and in correlating the information to produce output .", |
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| "section": "TEST SETTING S", |
| "sec_num": null |
| }, |
| { |
| "text": "As an NLP system improves and its knowledge of linguistics and the domain become mor e complete, confidence factors can be raised . Additionally, an arsenal of heuristics could be mad e available, depending on the confidence threshold assigned to the language processing task .", |
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| "section": "TEST SETTING S", |
| "sec_num": null |
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| { |
| "text": "Assignment of confidence ratings can be assisted by empirical data . For example, we can assign an overall system confidence in identifying the incident type by scoring performance fo r the entire MUC3 corpus .", |
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| "ref_spans": [], |
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| "section": "TEST SETTING S", |
| "sec_num": null |
| }, |
| { |
| "text": "Once the skimmer was completed (April 22), we spent approximately 2 man-month s customizing INLET to the MUC3 domain and task . A substantial portion of that time was spent i n implementing code for filling the various slots in the MUC3 template and in developin g heuristics to improve recall and precision . Somewhat less time was spent in building an d applying generic and domain-specific grammar rules . System testing was relatively painless , because INLET typically processed 100 messages in 45 minutes, and several Sun Sparcstation s were typically available for running tests . Unfortunately, we allowed too little time fo r vocabulary addition, so that many important words and phrases were omitted (e .g . , \"machinegun\", \"automatic weapons\") .", |
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| "section": "EXPENDITURE OF EFFOR T", |
| "sec_num": null |
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| "text": "Our main problems resulted from working with a new and incomplete system . Too ofte n we had to devote our time to fixing bugs or making improvements in the skimmer, in th e graphic representation tools, and in the knowledge addition tools . Starting with a smal l vocabulary and little linguistic and domain knowledge was disadvantageous . Adding a lot o f knowledge to the system over a short period of time caused many problems to surface (e .g., initializing the system became a time-waster, system tables overflowed several times) . Lack o f an internal representation for information extracted from the text was yet another limitation o n development .", |
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| "section": "LIMITING FACTOR S", |
| "sec_num": null |
| }, |
| { |
| "text": "We used the entire development corpus, including the key templates, for gatherin g domain information . For example, we used the key templates to get fairly complete lists o f perpetrators, targets, instruments, and so on . Similarly, we searched the corpus for keywords , temporal, locative, and other patterns . Many of our domain-specific grammar rules wer e crafted using the results of such searches .", |
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| "section": "TRAININ G", |
| "sec_num": null |
| }, |
| { |
| "text": "The first 100 messages of the development set served as a primary development an d testing vehicle . TST1 messages were run occasionally in order to gauge progress on unseen message sets .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "TRAININ G", |
| "sec_num": null |
| }, |
| { |
| "text": "In order to shake out bugs in the system, we processed half the development set in batches of 100 messages several days before the testing deadline .", |
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| "section": "TRAININ G", |
| "sec_num": null |
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| { |
| "text": "In general, skimming worked much better than expected . Based merely on our initial results for MUC3, we conclude that skimming is a powerful adjunct to deeper processing of text . We feel that with several months' work, continued development of skimming techniques , combined with knowledge base and vocabulary development, would substantially raise our MUC 3 score . Skimming provides extremely fast, simple, and robust text processing . While keyword and pattern-based methods for NLP have usually met with scorn, we feel a review of thes e methods is called for .", |
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| "section": "STRENGTHS AND WEAKNESSE S", |
| "sec_num": null |
| }, |
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| "text": "On the other hand, we are aware of the limitations of any approach that doesn't analyz e text as deeply as possible . In order to segment incidents with great accuracy, linguistic contex t as well as script-level understanding of the text are required . Many reference resolutio n problems also require such knowledge .", |
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| "section": "STRENGTHS AND WEAKNESSE S", |
| "sec_num": null |
| }, |
| { |
| "text": "In the near future, we will merge our skimming capability with a bottom-up syntacti c analysis mechanism, and also incorporate script-based understanding mechanisms .", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "STRENGTHS AND WEAKNESSE S", |
| "sec_num": null |
| }, |
| { |
| "text": "The INLET customization tools have proved their worth by supporting hierarchy , grammar rule, and vocabulary addition . Even our qualified success would have been impossibl e without the effectiveness of the knowledge addition framework .", |
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| "section": "STRENGTHS AND WEAKNESSE S", |
| "sec_num": null |
| }, |
| { |
| "text": "Our system is fairly good at determining the incident type, using a hierarchy of ke y words and patterns . With just a few specialized rules, the system is able to process appositive s to find perpetrators, perpetrator organizations, physical targets, and human targets . An extensive temporal grammar was developed, though not much correlation of multiple tempora l references has been implemented . A similar situation holds for locative phrases .", |
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| "eq_spans": [], |
| "section": "HITS AND MISSES", |
| "sec_num": null |
| }, |
| { |
| "text": "Simple gaps in knowledge and vocabulary caused many misses on the TST2 messages . Missing vocabulary (e .g., \"killings\"), missing domain rules (e .g., \"explosion caused damage\") , missing generic rules (e.g., \"actor participated in action on object\"), and missing mechanism s of various kinds led to substantially lower performance than we would expect of a more matur e INLET system . Missing mechanisms include lack of threat handling, lack of any inferencin g capability, lack of spelling correction, and lack of rejection of incidents for even simple reason s (e.g., an abstract object such as the \"economy\" is attacked) .", |
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| "section": "HITS AND MISSES", |
| "sec_num": null |
| }, |
| { |
| "text": "Because INLET is a customization shell, portability of the specific knowledge added to th e system is not a major concern . In 2 man-months, we were able to achieve a 25% recall and 35% precision score with a relatively immature INLET system . When the system is completed , we expect similar customization time to result in a better system for the particular domain an d task .", |
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| "section": "PORTABILIT Y", |
| "sec_num": null |
| }, |
| { |
| "text": "The skimmer framework and knowledge addition framework are generic, as is the core knowledge base and vocabulary . On top of this layer is a substantial body of domain-specifi c code and knowledge, which would necessarily have to be replaced for a new domain and task .", |
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| "section": "PORTABILIT Y", |
| "sec_num": null |
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| "text": "We have demonstrated that the INLET knowledge addition framework and skimmer ca n quickly support customization to a new domain and task . We have found that the graphic interface for knowledge addition has speeded up customization over a system like VOX . Finally , we have found that skimming is a critical adjunct to deeper NLP .", |
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| "section": "LESSONS LEARNED", |
| "sec_num": null |
| }, |
| { |
| "text": "Our participation in MUC3 has shown us the high value of formal testing and compariso n with other NLP efforts . We intend to continue using the MUC3 corpus and testing system for ou r system development, test, and evaluation .", |
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| "section": "LESSONS LEARNED", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [], |
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