| { |
| "paper_id": "M92-1024", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T03:13:23.240267Z" |
| }, |
| "title": "BBN: Description of the PLUM System as Used for MUC-4", |
| "authors": [ |
| { |
| "first": "Damaris", |
| "middle": [], |
| "last": "Ayuso", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "BBN Systems and Technologies", |
| "location": { |
| "addrLine": "10 Moulton Street Cambridge", |
| "postCode": "0213 8", |
| "region": "MA" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Sean", |
| "middle": [], |
| "last": "Boisen", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "BBN Systems and Technologies", |
| "location": { |
| "addrLine": "10 Moulton Street Cambridge", |
| "postCode": "0213 8", |
| "region": "MA" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Heidi", |
| "middle": [], |
| "last": "Fox", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "BBN Systems and Technologies", |
| "location": { |
| "addrLine": "10 Moulton Street Cambridge", |
| "postCode": "0213 8", |
| "region": "MA" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Herb", |
| "middle": [], |
| "last": "Gish", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "BBN Systems and Technologies", |
| "location": { |
| "addrLine": "10 Moulton Street Cambridge", |
| "postCode": "0213 8", |
| "region": "MA" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Robert", |
| "middle": [], |
| "last": "Ingria", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "BBN Systems and Technologies", |
| "location": { |
| "addrLine": "10 Moulton Street Cambridge", |
| "postCode": "0213 8", |
| "region": "MA" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Ralph", |
| "middle": [], |
| "last": "Weischede", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "BBN Systems and Technologies", |
| "location": { |
| "addrLine": "10 Moulton Street Cambridge", |
| "postCode": "0213 8", |
| "region": "MA" |
| } |
| }, |
| "email": "weisched@bbn.com" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "", |
| "pdf_parse": { |
| "paper_id": "M92-1024", |
| "_pdf_hash": "", |
| "abstract": [], |
| "body_text": [ |
| { |
| "text": "Traditional approaches to the problem of extracting data from texts have emphasized hand-crafted linguisti c knowledge . In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed a s part of a DARPA-funded research effort on integrating probabilistic language models with more traditiona l linguistic techniques . Our research and development goals are", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 more rapid development of new applications,", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 the ability to train (and re-train) systems based on user markings of correct and incorrect output, \u2022 more accurate selection among interpretations when more than one is found, an d", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 more robust partial interpretation when no complete interpretation can be found.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "A central assumption of our approach is that in processing unrestricted text for data extraction, a non-trivia l amount of the text will not be understood. As a result, all components of PLUM are designed to operate on partially understood input, taking advantage of information when available, and not failing when information is unavailable .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "We had previously performed experiments on components of the system with texts from the Wall Stree t Journal, however, the MUC-3 task was the first end-to-end application of PLUM. Very little hand-tuning of knowledge bases was done for MUC-4 ; since MUC-3, the system architecture as depicted in figure 1 has remained essentially the same.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "In addition to participating in MUC-4, since MUC-3 we focused on porting to new domains and a new language, and on performing various experiments designed to control recall/precision tradeoffs . To support these goals, the preprocessing component and the fragment combiner were made declarative; the semantics component was generalized to use probabilities on word senses ; we expanded our treatment of reference ; we enlarged the set of system parameters at all levels ; and we created a new probabilistic classifier for text relevance which filter s discourse events .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "BACKGROUND", |
| "sec_num": null |
| }, |
| { |
| "text": "The PLUM architecture is presented in Figure 1 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 38, |
| "end": 46, |
| "text": "Figure 1", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "SYSTEM ARCHITECTUR E", |
| "sec_num": null |
| }, |
| { |
| "text": "The input to the system is a file containing one or more messages . The preprocessing module determines message boundaries, identifies the header, and determines paragraph and sentence boundaries. The specification of the input format is now a declarative component of the preprocessor, which enables us to easily digest messages i n different formats. This component has proved its utility in porting to two non-MUC formats in the last year .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Preprocessin g", |
| "sec_num": null |
| }, |
| { |
| "text": "The first phase of the processing is assignment of part-of-speech information . In BBN's Fast Partial Parser (FPP) [2] , a bi-gram probability model, frequency models for known words (derived from large corpora), an d heuristics based on word endings for unknown words, assign part of speech to the highly ambiguous words of th e corpus . Since these predictions for unknown words were very inaccurate for input that is all upper case, w e augmented this part-of-speech tagging with probabilistic models (automatically trained) for recognizing words o f Spanish origin and words of English origin . This allowed us to tag new words that were actually Latin America n names highly reliably . The Spanish classifier uses a 5 character hidden Markov model, trained on about 30,00 0 words of Spanish text. The five-gram model of words of English was derived from text from the Wall Stree t Journal . ", |
| "cite_spans": [ |
| { |
| "start": 115, |
| "end": 118, |
| "text": "[2]", |
| "ref_id": "BIBREF4" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Morphological Analysis", |
| "sec_num": null |
| }, |
| { |
| "text": "The FPP is a deterministic stochastic parser which does not attempt to generate a single syntactic interpretation of the whole sentence, rather, it generates one or more non-overlapping parse fragments spanning the inpu t sentence, deferring difficult decisions on attachment ambiguities . FPP produces an average of seven fragments for sentences of the complexity seen in the MUC-4 corpus' .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing", |
| "sec_num": null |
| }, |
| { |
| "text": "Here are the 8 parse fragments generated by FPP for the first sentence of TST2-MUC4-0048 : (NP (NP (ADJP (ADJ \"SALVADORAN\") ) (N \"PRESIDENT-ELECT\") ) (NP (N \"ALFREDO\" \"CRISTIANI\")) ) (VP (AUX ) (VP (V \"CONDEMNED\") (NP (DETERMINER 'THE\" ) (N 'TERRORIST\" ) (N \"KILLING\") ) (PP (PREP \"OF\") (NP (NP (N \"ATTORNEY GENERAL\")) (N (NAME \"ROBERTO\") (NAME \"GARCIA\") (NAME \"ALVARADO\"))))))))) (\"AND\" (CONJ \"AND\")) (\"ACCUSED THE FARABUNDO MARTI NATIONAL LIBERATION FRONT\" (VP (AUX) (VP (V \"ACCUSED\" ) (NP (DETERMINER 'THE\" ) (N \"FARABUNDO\"\"MARTI\"'NATIONAL\"\"LIBERATION\"\"FRONT\")))) ) (PUNCT \"(\")) (\"FMLN\" (NP (N \"FMLN\"))) (\")\" (PUNCT \")\")) (\"OF THE CRIME\" (PP (PREP \"OF\" ) (NP (DETERMINER 'THE\") (N \"CRIME\"))) ) C .\" (PUNCT \" .\"))", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing", |
| "sec_num": null |
| }, |
| { |
| "text": "The semantic interpreter operates on each fragment produced by FPP in a bottom-up, compositional fashion . Throughout the system, defaults are provided so that missing semantic information or rules do not produce errors , but simply mark semantic elements or relationships as unknown . This is consistent with our belief that partia l understanding has to be a key element of text processing systems, and missing data has to be regarded as a normal event . The semantic component encompasses both lexical semantics and semantic rules . The semantic lexicon is separate from the parses lexicon and has much less coverage.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Interpreter", |
| "sec_num": null |
| }, |
| { |
| "text": "We used an automatic case frame induction procedure to construct an initial version of the lexicon [2] . Word senses of the semantic lexicon have probability assignments, which we plan to derive automatically from corpora . For MUC-4, probabilities were assigned so each word sense is more probable than the next sense of the word a s entered in the lexicon.", |
| "cite_spans": [ |
| { |
| "start": 99, |
| "end": 102, |
| "text": "[2]", |
| "ref_id": "BIBREF4" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Semantic Interpreter", |
| "sec_num": null |
| }, |
| { |
| "text": "(defverb \"BOMB\" ( BOMB-V-1 BOMBING (:case (subject PEOPLE TI-PERP-OF) (object ANYTYPE OBJECT-OF))) )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "This entry indicates that the type is BOMBING, that a subject argument whose type is PEOPLE should be given the role TI-PERP-OF, and that an object argument of any type should be given the role OBJECT-OF . BOMB-V-1 is the unique identifier of this (only) word sense.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "The semantic rules are based on general syntactic patterns, using wildcards and similar mechanisms to provide an extra measure of robustness . The basic elements of our semantic representation are \"semantic forms\", each of which introduces a variable (e .g. ? 13) with a type and a collection of predicates pertaining to that variable .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "There are three basic types of semantic forms: entities of the domain, events, and states of affairs . Each of these three can be further categorized as known, unknown, and referential. Entities correspond to the people, places , things, and time intervals of the domain . These are related in important ways, such as through events (who did wha t to whom) and states of affairs (properties of the entities) . Entity descriptions typically arise from noun phrases; events and states of affairs may be described in clauses .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "Not everything that is represented in the semantics has actually been understood. For example, the predicate PP-MODIFIER indicates that two entities (expressed as noun phrases) are connected via a certain preposition . In this way, we have a \"placeholder\" for the information that a certain structural relation holds between these two items , even though we do not know what the actual semantic relation is . Sometimes understanding the relation more full y is of no consequence, since the information does not contribute to the template-filling task . The information i s maintained, however, so that later expectation-driven processing can use it if necessary .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "Here is a semantic rule which handles, for example, \" group of businessmen \" , \"murder of a man\", and \"terrorist s of the FMLN \" :", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "For an NP dominating an NP1, and a PP whose PREP is \"OF\" and which dominates NP2 :", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "If NP1 is in (\"GROUP, \"BAND\" ) ; return semantics of NP2", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "If NP1 is an EVENT of type TERRORIST ; make NP2 the OBJECT-OF NP1 ; return new NP1 sem", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "If type of NP1 is PEOPLE and type of NP2 is ORGANIZATION, merge semantics, showing that NP1 BELONGS-TO NP2; otherwise use a more general NP => NP PP rule", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "An important consequence of the fragmentation produced by FPP is that top-level constituents are typically more shallow and less varied than full sentence parses. As a result, more semantics coverage was obtained early on in the development process with few semantic rules than would have been expected if the system had had to cover widely varied syntactic structures before producing any semantic structures . In this way, semantic coverage can be added gradually, while the rest of the system is progressing in parallel.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "After having assigned semantic representations to the fragments produced by FPP, it is often possible to mak e some of the attachment decisions which had been deferred. For example, it is possible to combine two NPs o f compatible semantic types that are conjoined, or attach prepositional phrases preferentially, using informatio n automatically derived from a corpus [7] . Our basic system uses fragment combination for certain proper nam e constructions, while some of our submitted optional runs used more extensive patterns for fragment combination . Figure 2 shows a graphical version of the semantics generated for the first fragment of S1 in TST2-MUC4-0048 . In this example note the UNKNOWN-EVENT created for the main verb \"CONDEMNED\", which has no lexica l semantics in our system, but still generates a useful semantic representation . ", |
| "cite_spans": [ |
| { |
| "start": 369, |
| "end": 372, |
| "text": "[7]", |
| "ref_id": "BIBREF1" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 556, |
| "end": 564, |
| "text": "Figure 2", |
| "ref_id": "FIGREF2" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Lexical semantic entries indicate the word's semantic type (a domain model concept), as well as predicate s pertaining to it. For example, here is the lexical semantics for the verb BOMB :", |
| "sec_num": null |
| }, |
| { |
| "text": "The discourse component of PLUM performs the operations necessary to construct event objects correspondin g to relevant events in the message . Each event object in the discourse event structure is similar in principle to th e notion of a \"frame\", with its corresponding \"slots\" or fields . The semantic representation of an event in the text onl y includes information contained locally in a fragment (after fragment combination); in creating corresponding even t objects, the discourse module must infer other long-distance or indirect relations not explicitly found by th e interpreter, and resolve any references in the text . The template generator then uses the structures created by th e discourse component to generate the final templates . Currently only terrorist incidents (and \"possible terrorist incidents\") generate discourse events, since these are the core events for MUC-4 template generation . The discourse component was further discussed in [1] . Two primary structures are created by the discourse processo r which are used by the template generator : the discourse predicate database and the event structure . The database contains all the predicates mentioned in the semantic representation of the message . When references are resolved , corresponding semantic variables are unified in the database . Any other inferences done by the discourse componen t also get added to the database.", |
| "cite_spans": [ |
| { |
| "start": 961, |
| "end": 964, |
| "text": "[1]", |
| "ref_id": "BIBREF0" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discourse Processing", |
| "sec_num": null |
| }, |
| { |
| "text": "To create the discourse event structure, the discourse component processes each semantic form produced by the interpreter, adding its information to the database and performing reference resolution when needed . Pronouns and person-type anaphoric definite NPs may be resolved. In addition, set-and member-type reference is also treated in other simpler domains. Some intra-sentential structural constraints on reference are enforced. When a semantic form for an event of interest is encountered, a discourse event is generated, and any slots already found by the interprete r are filled in the event. This event is then merged with a previous event if they are compatible . This heuristic assumes that the events were possibly derived from repeated references to a single real event .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discourse Processing", |
| "sec_num": null |
| }, |
| { |
| "text": "Once all the semantic forms have been processed, heuristic rules are applied to fill in any unfilled slots by looking at text surrounding the forms which triggered a given event. Each filler found is assigned a score based on where it was found in relation to an event trigger, indicating a higher confidence for fillers found closer to a trigger.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discourse Processing", |
| "sec_num": null |
| }, |
| { |
| "text": "Following is the discourse event structure for the first event in TST2-MUC4-0048 : Each trigger fragment contains one or more words whose semantics triggered this event . In the example above , a score of 0 indicates the filler was found directly by the semantics ; 1 that it was found in the same fragment as a trigger semantic form ; 2 in the same sentence ; 4 in the same paragraph; and 6 in an adjacent paragraph .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discourse Processing", |
| "sec_num": null |
| }, |
| { |
| "text": "Event", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discourse Processing", |
| "sec_num": null |
| }, |
| { |
| "text": "The template generator takes the event structure produced by discourse processing and fills out the applicationspecific templates. Clearly much of this process is governed by the specific requirements of the application , considerations which have little to do with linguistic processing. The template generator must address any arbitrar y constraints, as well as deal with the basic details of formatting .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Template Generation", |
| "sec_num": null |
| }, |
| { |
| "text": "The template generator uses a combination of data-driven and expectation-driven strategies . First the information in the event structure is used to produce initial values . At this point, values which should be filled i n but are not available in the event structure are supplied from defaults, either from the header (e .g ., date and location information) or from reasonable guesses (e.g. that the object of a murder is usually a suitable filler for the huma n target slot when the semantic type of the object is unknown).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Template Generation", |
| "sec_num": null |
| }, |
| { |
| "text": "We expect to eventually use a classifier at this stage of processing. This is especially appropriate for template slots with a set list of possible fillers, e .g . perpetrator confidence, category of incident, etc.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Template Generation", |
| "sec_num": null |
| }, |
| { |
| "text": "A new classifier for determining text relevance is now a component of PLUM . It may be utilized by our system to filter out a discourse event object when none of the phrases that gave rise to it is found in a paragraph classified as relevant . Since the event objects are the input lo the template generator, it serves effectively as a filte r on templates .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Text Relevance", |
| "sec_num": null |
| }, |
| { |
| "text": "The text classifier uses a probabilistic model to perform a binary classification . The features used by the model are stemmed words . The text classifier is trained automatically from two sets of text representing the categories of the classifier (i .e . relevant and irrelevant). A chi-square test is used to determine which words are good indicators of membership on one category but not the other. These words become the features of the probabilistic model. A log probability representing the likelihood of the word occurring in text of one type or the other is assigned to eac h word. It is this probability that is used in the classification process.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Text Relevance", |
| "sec_num": null |
| }, |
| { |
| "text": "When a piece of text is to be classified, it is scanned for occurrences of the word features selected durin g training. Summing the log probabilities of all the evidence found in the text gives a measure of the likelihood that the text is a member of a particular category. The sum is then compared to a user-selected threshold to determine the classification. Different thresholds produce different recall and precision values, allowing the user to tune th e classifier for high recall, high precision, or something in between . Several of our optional runs showing a wide range of recall-precision tradeoffs were obtained by varying the classifier threshold.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Text Relevance", |
| "sec_num": null |
| }, |
| { |
| "text": "An important feature of PLUM is that many aspects of its behavior can be controlled by simply varying th e values of system parameters . An important goal has been to make our system as \"parameterizable\" as possible, so that the same software can meet different demands for recall, precision, and overgeneration . PLUM has parameters to control, for example, some aspects of fragment combination, event merging and slot filling by discourse, an d relevance assignment by the classifier. In order to pick which system configuration to use for our required MUC-4 run, we tested more than 25 configurations on two test sets and one training set. Maximal F-scores were obtained with settings for aggressively merging events, conservatively looking for slot fillers, and a classifier threshold on relevance of 1 . \"ROBERTO GARCIA ALVARADO \" \"ATTORNEY GENERAL\": \"ROBERTO GARCIA ALVARADO \" GOVERNMENT OFFICIAL : \"ROBERTO GARCIA ALVARADO\" 1 : \"ROBERTO GARCIA ALVARADO\" DEATH : \"ROBERTO GARCIA ALVARADO \" TST2-MUC4-0048 2 -19APR89 EL SALVADOR: SAN SALVADOR (CITY) BOMBING ACCOMPLISHED 'THE BOMB \" \"EXPLOSIVES \" BOMB: 'THE BOMB \" EXPLOSIVE: \"EXPLOSIVES \" TERRORIST ACT \"GUERRILLAS\" \"FMLN \" REPORTED AS FACT : \"FMLN\" \"MERINO'S HOME\" CIVILIAN RESIDENCE : \"MERINO'S HOME \" 1 : \"MERINO'S HOME\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parameters in PLUM", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 We correctly identified the 2 events of interest Merino's murder and the attack on Merino's home.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "TEMPLATES FOR EXAMPLE MESSAGE", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 We found the correct instruments (though we overgenerated \"EXPLOSIVES\" in template 2).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "TEMPLATES FOR EXAMPLE MESSAGE", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 We filled the location, stage of execution, incident category, perpetrator organization correctly in both templates .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "TEMPLATES FOR EXAMPLE MESSAGE", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 We found the correct perpetrator individual ID in template 2 (and one could argue also in template 1) .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "TEMPLATES FOR EXAMPLE MESSAGE", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 We picked up the wrong dates. \u2022 Our conservative heuristics for picking up multiple targets did not find more than one correct target in thes e templates .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "However.", |
| "sec_num": null |
| }, |
| { |
| "text": "' This number is inflated due to the fact that sentence-final punctuation always appears as a separate fragment, an d the fact that commas frequently appear as isolated fragments .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Computational Aspects of Discourse in the Context of MUC-3", |
| "authors": [ |
| { |
| "first": "Et", |
| "middle": [], |
| "last": "Iwanska", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "Proceedings of the Third Message Understanding Conference (MUC-3)", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Iwanska, et.al ., \"Computational Aspects of Discourse in the Context of MUC-3\", Proceedings of the Third Message Understanding Conference (MUC-3), May 1991 . 0 . MESSAGE : ID 1. MESSAGE: TEMPLATE 2. INCIDENT : DATE 3. INCIDENT: LOCATION 4. INCIDENT: TYPE 5. INCIDENT: STAGE OF EXECUTION 6. INCIDENT: INSTRUMENT ID", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "INCIDENT: INSTRUMENT TYPE", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "INCIDENT: INSTRUMENT TYPE", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "MERINO'S HOME", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "PERP : INDIVIDUAL ID 10.PERP: ORG ID 11.PERP: ORG CONFIDENCE 12.PHYS TGT: ID 13.PHYS TGT : TYPE 14.PHYS TGT: NUMBER 15.PHYS TGT : FOREIGN NATION 16.PHYS TGT : EFFECT OF INCIDENT 17.PHYS TGT : TOTAL NUMBE R 18.HUM TGT : NAME 19.HUM TGT: DESCRIPTION 20, HUM TGT: TYPE 21. HUM TGT: NUMBER 22. HUM TGT : FOREIGN NATION 23. HUM TGT : EFFECT OF INCIDENT DEATH : \"-\" 24. HUM TGT : TOTAL NUMBER DESTROYED : \"MERINO'S HOME \"", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Partial Parsing, A Report on Work in Progress", |
| "authors": [ |
| { |
| "first": "R", |
| "middle": [], |
| "last": "Weischedel", |
| "suffix": "" |
| }, |
| { |
| "first": "D", |
| "middle": [ |
| "M" |
| ], |
| "last": "Ayuso", |
| "suffix": "" |
| }, |
| { |
| "first": "R", |
| "middle": [], |
| "last": "Bobrow", |
| "suffix": "" |
| }, |
| { |
| "first": "S", |
| "middle": [], |
| "last": "Boisen", |
| "suffix": "" |
| }, |
| { |
| "first": "R", |
| "middle": [], |
| "last": "Ingria", |
| "suffix": "" |
| }, |
| { |
| "first": "J", |
| "middle": [], |
| "last": "Palmucci", |
| "suffix": "" |
| } |
| ], |
| "year": 1991, |
| "venue": "Proceedings of the Fourth DARPA Speech and Natural Language Workshop", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Weischedel, R., Ayuso, D . M., Bobrow, R ., Boisen, S., Ingria, R., and Palmucci, J. \"Partial Parsing, A Report on Work in Progress \" , Proceedings of the Fourth DARPA Speech and Natural Language Workshop , February 1991 .", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "PLUM System Architectur e" |
| }, |
| "FIGREF1": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "" |
| }, |
| "FIGREF2": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "Example Semantic Representation for \"SALVADORAN PRESIDENT-ELECT ALFREDO CRISTIANI CONDEMNED THE TERRORIST KILLING OF ATTORNEY GENERAL ROBERTO GARCIA ALVARADO \"" |
| }, |
| "FIGREF3": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "MURDER Trigger fragments: \"SALVADORAN PRESIDENT-ELECT ALFREDO CRISTIANI CONDEMNED THE TERRORIS T KILLING OF ATTORNEY GENERAL ROBERTO GARCIA ALVARADO\" \"GARCIA ALVARADO, 56, WAS KILLED \" 'TTS FRONT GROUPS ARE RESPONSIBLE FOR THE IRRATIONA L VIOLENCE THAT KILLED ATTORNEY GENERAL GARCIA \" TI-PERP-OF: 'ITS FRONT GROUPS\" (score = 1) 'TT\" (score = 1) <=> \"THE FMLN\" \"URBAN GUERRILLAS\" (score = 2) EVENT-TIME-OF : \"JUNE 1\" (score = 6) EVENT-LOCATION-OF: \"DOWNTOWN SAN SALVADOR\" (score = 2) \"EL SALVADOR\" (score = 2 ) TI-INSTRUMENT-OF: \"A BOMB\" (score = 2) OBJECT-OF : \"ATTORNEY GENERAL ROBERTO GARCIA ALVARADO\" (score = 0) \"PRESIDENT OF THE LEGISLATIVE\" (score = 2) \"AN ARENA LEADER\" (score = 2)" |
| }, |
| "FIGREF4": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "TGT : TOTAL NUMBER 18.HUM TGT: NAME 19.HUM TGT: DESCRIPTION 20. HUM TGT: TYPE 21. HUM TGT: NUMBER 22. HUM TGT: FOREIGN NATION 23. HUM TGT: EFFECT OF INCIDENT 24. HUM TGT: TOTAL NUMBER" |
| } |
| } |
| } |
| } |
