| { | |
| "paper_id": "M92-1033", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:13:14.683924Z" | |
| }, | |
| "title": "PARAMAX SYSTEMS CORPORATION : DESCRIPTION OF THE PARAMAX SYSTEM USED FOR MUC-4", | |
| "authors": [ | |
| { | |
| "first": "Carl", | |
| "middle": [], | |
| "last": "Weir", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Valley Forge Lab s Paramax Systems Corporation Paoli", | |
| "institution": "", | |
| "location": { | |
| "country": "Pennsylvani" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Barry", | |
| "middle": [], | |
| "last": "Silk", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Valley Forge Lab s Paramax Systems Corporation Paoli", | |
| "institution": "", | |
| "location": { | |
| "country": "Pennsylvani" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "a weir 0prc .unisys.cor n 215-648-2369 INTRODUCTIO N This paper describes the Paramax MUC-4 data extraction system, a system with an analysis componen t implemented in CLIPS that provides a level of text understanding that falls somewhere between what is possible with conventional information retrieval techniques and deep linguistic analysis. The Paramax MUC-4 system is a reimplementation of ideas originally introduced in the KBIRD system , which was evaluated in MUC-3 [4, 5]. There are plans to incorporate linguistic analysis techniques int o the system, but the current effort is focussed on maximizing the data extraction capabilities of simpler methods. The Paramax MUC-4 system is depicted in Figure 1 .", | |
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| "abstract": [ | |
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| "text": "a weir 0prc .unisys.cor n 215-648-2369 INTRODUCTIO N This paper describes the Paramax MUC-4 data extraction system, a system with an analysis componen t implemented in CLIPS that provides a level of text understanding that falls somewhere between what is possible with conventional information retrieval techniques and deep linguistic analysis. The Paramax MUC-4 system is a reimplementation of ideas originally introduced in the KBIRD system , which was evaluated in MUC-3 [4, 5]. There are plans to incorporate linguistic analysis techniques int o the system, but the current effort is focussed on maximizing the data extraction capabilities of simpler methods. The Paramax MUC-4 system is depicted in Figure 1 .", | |
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| "section": "Abstract", | |
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| "text": "The Paramax MUC-4 system's architecture consists of three main processing components . An initial preprocessing component generates a set of CLIPS facts in which words, sentences, and paragraphs are identified . This component also computes the likelihood of relevant types of events being described in a given text . A core, rule-based analysis component implemented in CLIPS extracts whatever information can be inferred on the basis of the CLIPS facts generated by the preprocessor and the system's applicationspecific rule base . Finally, a third, template generation component sorts through all of the facts that have been inferred, merging descriptions of identical events and generating collections of templates in a n appropriate format .", | |
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| "section": "APPROACH AND SYSTEM DESCRIPTION", | |
| "sec_num": null | |
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| "text": "The preprocessing component is implemented in a programming language specifically designed for processing textual data called PERL . 2 During preprocessing, words, sentences, paragraphs and punctuation marks are delimited . In addition, every word in the text is checked against a set of keyword profiles . These profiles are pairs of words and conditional probabilities representing types of events : arsons, attacks, bombings, kidnappings, robberies, and murders . Although murders are not treated as a basic typ e of event in the MUC-4 task, the system nevertheless attempts to identify them in order to predict significant deaths . If a word occurring in a text also occurs in one of the event profiles with a probabilit y greater than 55%, then descriptions of instances of that type of event are asserted to likely occur in th e text . Because the MUC-4 training sample is small, the list of words determined to be highly predictive o f a given type of event is hand-edited to eliminate entries that have been assigned high scores because o f sparse data.", | |
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| "text": "2", | |
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| "section": "The Preprocessing Component", | |
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| "text": "The preprocessor's average elapsed processing time per text in the MUC-4 TST3 data set of 100 text s is less than 3 seconds . Total elapsed preprocessing time for the data set is 4 minutes, 8 seconds . It too k approximately three days to write the preprocessor, and another three or four days to create and tune th e event profiles it uses . ", | |
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| "section": "The Preprocessing Component", | |
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| "text": "After the text preprocessor has tokenized a text and has predicted which types of events are likely to b e described, the task of extracting information about instances of the predicted event types falls to a CLIPSbased analysis system called CBAS . CLIPS is a forward-chaining system developed at NASA's Johnso n Space Center that has received a lot of attention lately because of its speed and low cost . Rule-base d systems similar to CLIPS have been used before to implement data extraction systems ; two well-know n implementations of this sort are the Carnegie Group's Text Categorization Shell [3] and the ADS Rubric system, which is a subcomponent of the Codex system evaluated at MUC-3 [1] .", | |
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| "text": "[3]", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "CBAS rule format . An important feature of CBAS rules is that the facts which the rules infer ar e associated with specific regions of text in very much the same way that edges in a parser's well-forme d substring table are assigned to specific regions of an input string . In the KBIRD system referred to earlier , the region assigned to an inferred fact is automatically computed to be the maximal cumulative span o f the regions of text associated with each expression in the antecedent of the rule making the inference. In CBAS, this region must be explicitly specified . Giving up the automated computation of the regio n provides more flexibility.", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "Unlike typical parsers, which contain an implicit constraint that adjacent constituents in a rule must b e realized by contiguous strings of text in the input, CBAS requires all constraints to be explicitly encoded . In the earlier KBIRD system, constraints were realized using operators expressing relationships amon g the text regions associated with facts . In CBAS, relationships are expressed via constraints on attribute s of facts rather than with operators taking facts as arguments .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "The following example of a CBAS rule states that if the text preprocessor has predicted the likel y occurrence of a bombing event and if the lexical item dynamited occurs in the text being analysed, the n a bombing event instance is to be predicted at the same location as the occurrence of the lexical item : The following two CBAS rules illustrate how rudimentary phrases can be constructed . In this case , proper names are recognized through examination of a database of known names, and proper names tha t are next to one another are concatenated together to form compound phrases . Processing Phases/Rule modules . Following standard practice in forward-chaining system development, the antecedent portions of CBAS rules include references to control fact statements . These control facts are asserted and retracted during the processing of a text to enable or disable portions of the Ret e network constructed out of the system's rule base .' Through the use of control facts, different inferencin g phases can be defined and the rules associated with these phases constitute separate modules, some o f which may be used in other domains and/or applications . Three different types of rule modules arise :", | |
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| "text": "Event attribute rule modules . Modules of this sort consist of rules which infer' facts describing possible properties of events without associating those properties with any specific event instance . For example, a rule module exists whose only goal is to identify proper name expressions . Similarly, a rule module exists that identifies possible physical targets .", | |
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| "text": "Event instance rule modules . Modules of this sort consist of rules which locate instances of types o f events . There is only one module of this sort in the MUC-4 implementation, but multiple modules of this sort could exist . For example, it might be desirable to have separate modules for each even t type .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "Slotvaluerule modules . Modules of this sort consist of rules which infer possible slot values for templates describing event instances . Facts inferred by rules in this type of module are weighted .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "Key CBAS features . The most significant features of CBAS are the following :", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 CBAS is implemented in CLIPS, which can be acquired at little or no cost .4", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 The implementation is reasonably fast, with an average processing time per text in the MUC-4 TST 3 data set of 1 minute, 46 seconds . Total processing time for all 100 messages in the data set is 2 hours, 57 minutes . '", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
| "sec_num": null | |
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| "text": "\u2022 The implementation was built quickly, in about 2 months with less than 4 person-months of effort . '", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 The CBAS rule formalism is easy to learn and requires no linguistic expertise . Most of the rules written for the MUC-4 prototype were written by someone with no background in linguistics .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "Template Selection . The process of determining which template structures to build out of the fact s inferred by CBAS begins by determining if any events at all have been predicted . If no event has been predicted, then an \"irrelevant template\" is created . If several events of the same type have been created , the template. generator will attempt to merge them using a set of heuristics which hypothesize that tw o event descriptions refer to the same event . Some of the general heuristics used for merging events of the same class are the following :", | |
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| "text": "\u2022 Merge two events if there is a significant overlap in the text regions found by the event locator rules .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 Merge two events if they share human targets whose scores are above a certain threshold .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 Merge two events if they share physical targets whose scores are above a certain threshold .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "Slot Filler Selection . After merging events, the template generator must select the final slot filler values . The CBAS rules which propose slot fillers attach a score (an integer between 0 and 100) to eac h candidate which represents the system's confidence in that value . If multiple candidate fillers exist for a given template, several general heuristics are used to select among them :", | |
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| "text": "\u2022 Candidate slot values with scores below a given threshold are dropped from consideration .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| { | |
| "text": "\u2022 A set of synonymous expressions are dropped in favor of their canonical expression .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 If one candidate expression is a substring of another, then the shorter one is dropped .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 A generic description (e .g ., vehicles) is dropped in favor of one or more subsumed ones (e .g ., ambulance, truck) .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "\u2022 If a slot can only take a single value then the candidate receiving the highest value is selected .", | |
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| "section": "A CLIPS-Based Analysis System (CBAS )", | |
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| "text": "The template generator is fast, with an average processing time per text in the MUC-4 TST3 data se t of 11 seconds . Total processing time for all 100 texts in the MUC-4 TST3 data set was 17 minutes, 4 4 seconds.", | |
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| "text": "In this section, we illustrate in a more concrete fashion how the Paramax MUC-4 system goes abou t processing messages by examining in detail what happens during the processing of a specific text, messag e TST2-MUC4-0048, in the MUC-4 corpus . ? Our discussion will proceed through the three stages of tex t processing that have been identified . Figure 2 contains a sampling of the CLIPS text facts created during the preprocessing stage for TST2 -MUC4-0048 . The msgsocation, msg .date, and msg .src facts are extracted from the text dateline . Th e word facts identify the locations of lexical items, and the ss and pp facts identify the locations of sentenc e and paragraph boundaries, respectively . For this message, three types of events were predicted : attacks , bombings, and murders.", | |
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| { | |
| "start": 347, | |
| "end": 355, | |
| "text": "Figure 2", | |
| "ref_id": null | |
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| "eq_spans": [], | |
| "section": "AN EXTENDED EXAMPL E", | |
| "sec_num": null | |
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| { | |
| "text": "At the start of the second stage of processing, the set of CLIPS facts generated during the preprocessin g stage are asserted to the CBAS factbase . Once this is done, the forward-chaining engine is invoked to extract information .", | |
| "cite_spans": [], | |
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| "section": "Second Stage : CLIPS-Based Analysi s", | |
| "sec_num": null | |
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| { | |
| "text": "? Appendix F contains the text and answer key templates for TST2-MUC4-0048 .", | |
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| "section": "Second Stage : CLIPS-Based Analysi s", | |
| "sec_num": null | |
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| { | |
| "text": "(msgiocation (id \"SAN SALVADOR\") ) (msg .date (day \"19\")(month \"APR\")(year \"89\") ) (msgsrc (lex \"ACAN-EFE\") ) (word (pp 1) (ss 1) (left 0) (right 1) (lex \"SALVADORAN\") ) (word (pp 1) (ss 1) (left 1) (right 2) (lex \"PRESIDENT-ELECT\") ) (word (pp 1) (ss 1) (left 2) (right 3) (lex \"ALFREDO\") ) (word (pp 1) (ss 1) (left 3) (right 4) (lex \"CRISTIANI\") ) (word (pp 2) (ss 2) (left 26) (right 27) (lex \"LEGISLATIVE\") ) (word (pp 2) (ss 2) (left 27) (right 28) (lex \"ASSEMBLY\") ) (word (pp 2) (ss 2) (left 28) (right 29) (lex \"PRESIDENT\") ) (word (pp 2) (ss 2) (left 29) (right 30) (lex \"RICARDO\") ) (ss (num 1) (pp 1) (left 0) (right 26) ) (ss (num 2) (pp 1) (left 26) (right 84) ) (pp (num 1) (left 0) (right 26) ) (pp (num 2) (left 26) (right 55) ) (predicted\u00b1vent_type (type \"ATTACK\") ) (predictedeventlype (type \"BOMBING\") ) (predictedeventtype (type \"MURDER\") ) Figure 2 : Some of the preprocessing output for Message TST2-MUC4-0048 .", | |
| "cite_spans": [], | |
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| { | |
| "start": 862, | |
| "end": 870, | |
| "text": "Figure 2", | |
| "ref_id": null | |
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| "section": "Second Stage : CLIPS-Based Analysi s", | |
| "sec_num": null | |
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| "text": "Since linguistic analysis techniques are not incorporated into the system, information about subconstituent structure is limited . Constraints of this sort on the system's data extraction capabilities ar e inherent constraints . The system also contains accidental constraints, which are caused by engineerin g flaws in the system and not by inherent limitations in the extraction methods being employed .", | |
| "cite_spans": [], | |
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| "section": "Second Stage : CLIPS-Based Analysi s", | |
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| "text": "It is often not obvious that a data extraction problem is a consequence of an inherent constraint o r an accidental one, or if it is a consequence of some combination of inherent and accidental constraints . From the perspective of the MUC-4 conference, detecting inherent constraints is more interesting tha n detecting accidental constraints . Keeping this in mind, performance characteristics of the CBAS analysi s component will now be described by examining some of the slot values extracted by the system for messag e TST2-MUC4-0048 .", | |
| "cite_spans": [], | |
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| "section": "Second Stage : CLIPS-Based Analysi s", | |
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| { | |
| "text": "Because of the event type predictions made during the preprocessing stage , the system is primed to look for instances of attacks, murders, and bombings in TST2-MUC4-0048 . In the CBAS system, each reference to a given type of event detected in a text is treated as if it is referring to a different event instance . It is up to the template generation component to determine that the template s describing different event instances are in fact describing the same event . A key problem with the MUC-4 implementation is that this identification process is not working well, leading to an excessive number o f spurious values .", | |
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| "section": "Event instance location .", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "For TST2-MUC4-0048, the MUC-4 CBAS system generated 4 response templates . The first template describes a terrorist act identified as an attack, but for which a bomb is correctly identified as an instrument . Given the presence of a bomb as an instrument, the failure to identify the event as a bombing i s clearly the consequence of an indequately developed rule base and not the result of any inherent constrain t in the methodology being used .", | |
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| "section": "Event instance location .", | |
| "sec_num": null | |
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| "text": "It is important to keep in mind that many event instances are created during the data extraction process , and that the goal is to identify instances of the same actual event and merge their template structures . A number of bombing event instances were generated for this message, as expected given the number o f references to bombs and explosions . However, since the event instances leading to the generation of the first response template were identified as attacks, their template structures were not merged with those o f bombing instances . A total of three other spurious templates all of which were identified as description s of bombings, were generated for the sample text .", | |
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| "section": "Event instance location .", | |
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| "text": "The second problem evidenced in the extraction of physical targets is the presence of \" HOME\" as a possible value, along with \"CAR\" and \"ARMORED VEHICLE\" . It turns out that the CBAS rule bas e proposed two separate attack event instances, one for the bomb attack on Alvarado 's car, and one fo r the bomb attack on Merino ' s home . However, the template generator component, which is responsibl e for merging templates describing the same event, erroneously merged the templates describing these tw o separate attack instances, combining their physical target slot values . Not enough time was available to develop template merging heuristics .", | |
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| "section": "Event instance location .", | |
| "sec_num": null | |
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| "text": "Extracting Human Targets . A number of spurious human target values are proposed in the firs t response template . Many of these spurious values have arisen because of a poorly tuned rule base and no t because of any inherent contraints in the analysis techniques employed . If finer-grained rule weightings were to be employed so that strings describing human targets which are located relatively near a region o f text triggering the detection of an event instance are preferred over others, then many of these spuriou s values would not be present .", | |
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| "section": "Event instance location .", | |
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| "text": "The human targets \"RICARDO VALDIVIESO\" \"ROBERTO GARCIA ALVARADO\", \"LEADER\" , \"JUSTICE\", \"DRIVER\", and \"BODYGUARDS\" were all proposed with equal likelihood as possibl e slot values for an event instance detected through the occurrence of the word \"KILLED\" in the phrase \"VIOLENCE THAT KILLED ATTORNEY GENERAL GARCIA . \" If a higher score were assigned t o human targets immediately following \"KILLED\", then the other values could be eliminated . This could most certainly be done, and in fact was done in the KBIRD implementation described earlier . For thi s type of finer-grained score assignment, it would make sense to employ a part-of-speech tagger that is able to distinguish simple past tense forms from past participles used in passive constructions .8", | |
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| }, | |
| { | |
| "text": "It is less clear how to reintroduce \"DRIVER\" and \"BODYGUARDS\" as slot values, given that the y will not be introduced if the suggested improvements in rule scoring are made . It is in the following two sentences that it is made clear that the driver and bodyguards were also targets : \"ACCORDING T O THE POLICE GARCIA ALVARADO'S DRIVER, WHO ESCAPED UNSCATHED, THE ATTORNE Y GENERAL WAS TRAVELING WITH TWO BODYGUARDS . ONE OF THEM WAS INJURED .\" Rules could certainly be written to capture these additional human targets . For example, rules could be sensitiv e to the existence of individuals who \"ESCAPE UNSCATHED\" and to groups of individuals, some of who m are injured and that are \"TRAVELING WITH\" a highly likely human target . But it is not clear if one would ever be able to write enough rules in a given domain to reach closure in extracting all human target s that are referred to in this sort of indirect fashion .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Event instance location .", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The string \"URBAN TERRORISTS VICE PRESIDENT-ELECT\" arises as a reference to a huma n target because of a bug in one of the CBAS rules that builds up compound common noun phrases . In this case, the rule was not written to pay attention to sentence boundaries, and consequently the common noun \"URBAN TERRORISTS\" occurring at the end of one sentence and \"VICE PRESIDENT-ELECT \" occurring at the beginning of the next sentence were incorrectly identified as a single common nou n expression . Correcting this type of error is easy. Third Stage : Template Generatio n Time and funding constraints made it impossible to implement a satisfactory template generation component for this system .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Event instance location .", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In the previous discussion of the analysis component's performance, references were made to cases in which the template generator merged template descriptions when it shouldn 't have . In many cases , the opposite is true . For example, two of the four templates generated for TST2-MUC4-0048 are exac t duplicates of one another . Moreover, the duplicates are relatively impoverished, containing no physica l targets, and only one reference to \"BODY\" as a human target descriptor . With a little cooperation fro m the rule base to eliminate \"BODY\" as a human target, the generator should be able to justify not printin g the underpopulated templates, and in any case, an existing heuristic for not printing exact duplicates o f templates needs to be fixed .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Event instance location .", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A key motivating factor in the design of the Paramax MUC-4 system has been a desire to exploit simple data extraction methods to the fullest extent possible . Another motivating factor has been the desir e to build a relatively inexpensive system that individuals with no training whatsoever in linguistics coul d develop and maintain .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CONCLUSION S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We do not believe that we have fully exploited the capabilities of non-linguistic data extraction method s and intend to continue to explore the simpler techniques used in the CBAS system . This intention i s not based on a strongly-held desire to avoid the use of linguistic-analysis techniques in data extraction applications . Quite the contrary-our research group is actively involved in the development of linguisti c analysis techniques and feels that they will ultimately be essential in data extraction applications . Ou r MUC-4 implementation is part of a long-term effort to better understand exactly when such techniques are in fact required .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CONCLUSION S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Except for the template generator, which is written in Prolog, the Paramax MUC-4 system has bee n built using freeware (PERL and CLIPS) . Each component is written in a different programming language , which is a strong indicator of its modularity .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CONCLUSION S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Finally, we have demonstrated by example that the approach taken in the Paramax MUC-4 system doe s not require any linguistic expertise . A general knowledge of the domain is all that is required . Acquirin g an understanding of the CBAS rule formalism and of CLIPS in general takes very little time-the tw o individuals that developed the MUC-4 system had no prior experience whatsoever with CLIPS .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CONCLUSION S", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": ". It prints the actual templates in the proper format .6 A Rete network is a data structure commonly used to encode information in forward-chaining systems . See Forgy[2] fo r an explanation of Rete networks .4 It can be acquired at no cost for NASA and USAF projects and at a marginal cost for all other uses ($490.00, including a three volume set of documentation) .'Total processing time including non-CLIPS processing (PERL preprocessing and PROLOG template generation) is 3 2 hours for the TST3 data set .6 We did not remember to actually write down the date we started . We have estimated two months because we know tha t CLIPS was installed at our site on 7 April 92 and we know that we stopped development on 30 May 1992 . We had access to some old rulebases that were created for the MUC-3 KBIRD system, but these were expressed in a sufficiently differen t rule formalism to require a total rewrite . We estimate less than 4 person-months of effort because only two individuals wer e involved, and both individuals were required to perform other tasks in addition to their work on MUC-4 .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Unfortunately, we have been told that taggers generally perform poorly in discriminating passive past participles fro m simple past tense forms and participles used in past perfect constructions .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "MESSAGE : ID TST2-MUC4-0048 1 . MESSAGE : TEMPLATE 1 2.INCIDENT : DATE 14 APR 8 9 3 .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Fille r O .", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "INCIDENT : TYPE ATTAC K 5 .INCIDENT : STAGE OF EXECUTION ACCOMPLISHED 6 .INCIDENT : INSTRUMENT ID \"BOMB \" 7 .INCIDENT : INSTRUMENT TYPE BOMB : \"BOMB\" 8 .PERP : INCIDENT CATEGORY TERRORIST AC T 9 . PERP : INDIVIDUAL ID \"AN INDIVIDUAL \" 10. PERP : ORG ID \"FARABUNDO MARTI NATIONAL LIBERATION FRONT \" 11 . PERP : ORG CONF CLAIMED OR ADMITTED : \"FARABUNDO MARTI NATIONAL LIBERATION FRONT \" 12. PHYS TGT : ID \"HOME \" \"CAR \" \"ARMORED VEHICLE\" 13.PHYS TOT : TYPE CIVILIAN RESIDENCE : \"HOME \" TRANSPORT VEHICLE : \"CAR \" TRANSPORT VEHICLE : \"ARMORED VEHICLE\" 14 .PHYS TOT : NUMBER 1 : \"HOME \" 1 : \"CAR\" 1 : \"ARMORED VEHICLE\" 15 .PHYS TOT : FOREIGN NATIO N 16 .PHYS TOT : EFFECT OF INCIDENT SOME DAMAGE : \"ARMORED VEHICLE \" SOME DAMAGE : \"CAR\" SOME DAMAGE : \"HOME \" 17 .PHYS TOT : TOTAL NUMBER 18 .HUM TOT : NAME \"ROBERTO GARCIA ALVARADO \" \"RICARDO VALDIVIESO \" \"ALFREDO CRISTIANI \" 19 .HUM TGT : DESCRIPTION \"LEADER \" \"JUSTICE\" \"DRIVER\" \"BODYGUARDS\" \"URBAN TERRORISTS VICE PRESIDENT-ELECT \" 'INDIVIDUAL \" \"SALVADORAN PRESIDENT-ELECT\" \"ATTORNEY GENERAL\" : \"ROBERTO GARCIA ALVARADO \" \"PRESIDENT\" : \"RICARDO VALDIVIESO\" \"PRESIDENT\" : \"ALFREDO CRISTIANI \" 20 .HUM Extracting perpetrator ids . In the first response template generated for TST2-MUC4-0048, the strin g \"AN INDIVIDUAL\" was recorded as the perpetrator id, based on the occurrence of this phrase in the the clause \"AN INDIVIDUAL PLACED A BOMB ON THE ROOF OF THE ARMORED VEHICLE\" . The correct string fill, \"URBAN GUERRILLAS\" was also extracted by CBAS, but the former value wa s assigned a higher score . The following ground clauses were generated by CBAS to represent these strings :potential_ind_perpetrator('TERRORISTS',\" 'URBAN GUERRILLAS\") . potential_ind_perpetrator('UNKNOWN',\" 'AN INDIVIDUAL\") .Unfortunately, the type specification for \"URBAN GUERRILLAS\" should have been output as 'TER-RORIST ' , not 'TERRORISTS' . Had this been done, then the following rule would have assigned a highe r score to \"URBAN GUERRILLAS\" . Since no rules exist for assigning probabilities to possible perpetrators of type \"TERRORISTS\", th e following CBAS rule assigned a relatively low score to \"URBAN GUERRILLAS \" as a possible fill fo r slot 9 : The following rule assigned a much higher weight to \"AN INDIVIDUAL\" : This type of error in the rule base could have been caught with a bit more staffing and/or developmen t time and is characteristic of relatively simple bugs that have a significant cumulative impact on dat a extraction performance .Extracting physical targets . Two separate problems are manifested in the slot values proposed for physical targets in the first response template generated for TST2-MUC4-0048 .The first problem is evidenced by the failure of the system to determine that \"CAR\" and \"ARMORED VEHICLE\" are co-referential . The ability to establish co-referentiality is minimal in the CBAS system , and this weakness is probably inherent in the methodology . However, in this particular case, the CBA S rule base could be expanded to treat \"CAR\" and \"ARMORED VEHICLE\" as synonyms and therefor e submit only one of them as a reference to a physical target . Were this done, the problem that would the n arise is the need to recognize when two separate objects are in fact being talked about .", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 158, | |
| "end": 400, | |
| "text": ": INCIDENT CATEGORY TERRORIST AC T 9 . PERP : INDIVIDUAL ID \"AN INDIVIDUAL \" 10. PERP : ORG ID \"FARABUNDO MARTI NATIONAL LIBERATION FRONT \" 11 . PERP : ORG CONF CLAIMED OR ADMITTED : \"FARABUNDO MARTI NATIONAL LIBERATION FRONT \" 12.", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "INCIDENT : LOCATION EL SALVADOR : SAN SALVADOR (DEPARTMENT) : SAN SALVADOR (CITY ) 4 .", | |
| "sec_num": null | |
| } | |
| ], | |
| "bib_entries": { | |
| "BIBREF0": { | |
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| "raw_text": "Laura Blumer Balcom and Richard M . Tong . Advanced decision systems : Description of the Codex system as used for MUC-3 . In Proceedings of the Third Message Understanding Conference, pages 129-136, San Diego, May 1991 . Morgan Kaufmann Publishers, Inc .", | |
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| "BIBREF2": { | |
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| "title": "Text categorization shell . Technical brief, Carnegie Group, Five PPG Place", | |
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| "title": "Unisys : Description of the Unisys syste m used for MUC-3", | |
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| "first": "Carl", | |
| "middle": [], | |
| "last": "Weir", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tim", | |
| "middle": [], | |
| "last": "Finin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Robin", | |
| "middle": [], | |
| "last": "Mcentire", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Barry", | |
| "middle": [], | |
| "last": "Silk", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1991, | |
| "venue": "Proceedings of the Third Message Understanding Conference", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "212--222", | |
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| "urls": [], | |
| "raw_text": "Carl Weir, Tim Finin, Robin McEntire, and Barry Silk . Unisys : Description of the Unisys syste m used for MUC-3 . In Proceedings of the Third Message Understanding Conference, pages 212-222, San Diego, May 1991 . Morgan Kaufmann Publishers, Inc .", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "Unisys : MUC-3 test results and analysis", | |
| "authors": [ | |
| { | |
| "first": "Carl", | |
| "middle": [], | |
| "last": "Weir", | |
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| }, | |
| { | |
| "first": "Robin", | |
| "middle": [], | |
| "last": "Mcentire", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Barry", | |
| "middle": [], | |
| "last": "Silk", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tim", | |
| "middle": [], | |
| "last": "Finin", | |
| "suffix": "" | |
| } | |
| ], | |
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| "venue": "Proceedings of the Third Message Understanding Conference", | |
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| "issue": "", | |
| "pages": "112--115", | |
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| "raw_text": "Carl Weir, Robin McEntire, Barry Silk, and Tim Finin . Unisys : MUC-3 test results and analysis . In Proceedings of the Third Message Understanding Conference, pages 112-115, San Diego, May 1991 . Morgan Kaufmann Publishers, Inc .", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF2": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "A flowchart of the Paramax MUC-4 system .", | |
| "num": null | |
| }, | |
| "FIGREF3": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "lex \"DYNAMITED\")(pp ?P)(ss ?S)(left ?L)(right ?R) ) _> (assert (bombing (id =(gensym))(key \"DYNAMITED\")(pp ?P)(ss ?S)(left ?L)(right ?R))) )", | |
| "num": null | |
| }, | |
| "FIGREF4": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "defrule assert-proper-nam e (control-fact (phase hum-proper) ) (word (lex ?LEX)(pp ?P)(ss ?S)(left ?L)(right ?", | |
| "num": null | |
| }, | |
| "FIGREF5": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "The string \"SALVADORAN PRESIDENT-ELECT\" should have been recognized as a title of Alfred o Cristiani in the phrase \u2022\"SALVADORAN PRESIDENT-ELECT ALFREDO CRISTIANI\" . A bug in on e of the CBAS rules that recognizes titles next to proper names was to blame . If a common noun expression is recognized as a title, it is not proposed as a separate reference to an individual . The presense of Alfredo Christiani as a possible victim could have been eliminated by the improvement in scoring technique s mentioned earlier .Finally, the string \"INDIVIDUAL\" arises as a reference to a human target . This error could have been eliminated by improved scoring techniques . Also, since \"AN INDIVIDUAL\" occurs as a perpetrator , improved template merging heuristics should have eliminated one of the values as a candidate filler .", | |
| "num": null | |
| }, | |
| "TABREF0": { | |
| "num": null, | |
| "type_str": "table", | |
| "text": "Barry Silk is a U.S . government employee on sabbatical at Paramax . 2 PERL is available at no cost ; it can be FTPed from prep .ai.mit .edu .", | |
| "content": "<table><tr><td/><td colspan=\"2\">O . MLBBAO\u2022 ID</td><td>TOP2-MOC4-001 8</td></tr><tr><td/><td colspan=\"2\">1 . TYPIATM</td><td>1</td></tr><tr><td/><td colspan=\"2\">2 . IRCZDRISP .7T9</td><td>-19 APR 8 9</td></tr><tr><td/><td>3 .</td><td>ICIO\u00aeR LOCATION</td><td>m RALVAODR, OAR SALVADOR (CITY )</td></tr><tr><td/><td>4 .</td><td>IRCIDOR TIP!</td><td>BOI11110</td></tr><tr><td/><td colspan=\"3\">S . SPA . OP RICRZW ACCORISmm</td></tr><tr><td/><td colspan=\"2\">6 . IROPmI\u00aeR ID</td><td>\u20228010\u2022</td></tr><tr><td/><td colspan=\"2\">7 . IROPROImR TYP\u2022</td><td>DOME, \u202280118\u2022</td></tr><tr><td/><td colspan=\"2\">8 . IRCIDOR CATEGORY</td><td>TIMAORIST ACT</td></tr><tr><td/><td colspan=\"2\">9 . PRR ID</td><td>\u2022ORBAR OORRRILI.AB \u2022</td></tr><tr><td/><td colspan=\"2\">10 . PBRP ORO ID</td><td>0000)\u2022</td></tr><tr><td/><td colspan=\"2\">11 . PBRP OPO CORP</td><td>SWPCTRD, \u2022PRIM\u2022</td></tr><tr><td/><td colspan=\"2\">12 . POTS ID</td><td>\u2022SR\u2022</td></tr><tr><td/><td colspan=\"2\">13 . POTS TYPE</td><td>V\u00aeZd\u2022, \u2022CAR\u2022</td></tr><tr><td/><td colspan=\"2\">11 . P9YB ROM</td><td>1</td></tr><tr><td/><td>IS .</td><td>PITS III.</td></tr><tr><td/><td>16 .</td><td>PRTB \u2022PPBCT</td><td>ROI\u2022 DAY.. v\u00ae1CZJ</td></tr><tr><td/><td colspan=\"2\">17 . PRYR TOPAZ mR</td></tr><tr><td/><td colspan=\"2\">18 . ROM DAR</td><td>\u2022ROIm9O ALVARADO \u2022</td></tr><tr><td/><td colspan=\"2\">19 . ROM D\u2022SCRIPPIOI</td><td>\u2022ATIWWPY\u2022, \u2022ROBORO ALVARADO \u2022</td></tr><tr><td/><td colspan=\"2\">20 . ROM TTP\u2022</td><td>LROALn \u2022ROB\u2022RPO ALVARADO \u2022</td></tr><tr><td/><td colspan=\"2\">21 . MRS NMI</td><td>\u2022SOBRRO ALVARADO \u2022</td></tr><tr><td/><td colspan=\"2\">22 . MDR WTI .</td></tr><tr><td/><td colspan=\"2\">OI . RUN IPPLCT</td><td>DRAW, \u2022RORRP O ALVAMDO\u2022</td></tr><tr><td/><td colspan=\"2\">21 . ROM TOTAL MI</td><td>-</td></tr><tr><td/><td/><td/><td>4</td></tr><tr><td/><td>r,;GryOei$hS</td><td/></tr><tr><td>Event</td><td>lot</td><td/></tr><tr><td>Instance</td><td colspan=\"2\">Value</td></tr><tr><td>Locatio n</td><td colspan=\"2\">Inference</td></tr><tr><td>Rules</td><td colspan=\"2\">Rules</td></tr></table>", | |
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