| { |
| "paper_id": "M95-1019", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T03:12:57.842552Z" |
| }, |
| "title": "SRI INTERNATIONAL FASTUS SYSTE M MUC-6 TEST RESULTS AND ANALYSI S", |
| "authors": [ |
| { |
| "first": "Douglas", |
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| "E" |
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| "last": "Appelt", |
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| "institution": "SRI International Menlo Park", |
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| "postCode": "9402 5", |
| "region": "California" |
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| "email": "appelt@ai.sri.com" |
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| "first": "Jerry", |
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| "last": "Hobbs", |
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| "abstract": "INTRODUCTIO N SRI International participated in the MUC-6 evaluation using the latest version of SRI's FASTUS system [1]. The FASTUS system was originally developed for participation in the MUC-4 evaluatio n [3] in 1992, and the performance of FASTUS in MUC-4 helped demonstrate the viability of finit e state technologies in constrained natural-language understanding tasks. The system has undergon e significant revision since MUC-4, and it is safe to say that the current system does not share a singl e line of code with the original. The fundamental ideas behind FASTUS, however, are retained i n the current system : an architecture consisting of cascaded finite state transducers, each providin g an additional level of analysis of the input, together with merging of the final results. This paper will describe the version of the FASTUS system employed in MUC-6 and highlight the innovations that distinguish it from previous versions described in the literature. SRI used the FASTUS system for each of the MUC-6 tasks : the named entity task, the templateentity task, the coreference task, and the scenario template task. Because a single system, with a single configuration, was used to run all the tasks, and because the first three tasks are in som e sense prerequisites to the fourth, we will focus our attention in this paper on the scenario templat e task. BASIC FASTUS The SRI FASTUS system is based on a series of finite-state transducers that compute the transformation of text from sequences of characters to domain templates. This architecture has proven t o be very flexible, and has been applied with success to a number of different information extractio n tasks in widely varying domains. We have applied FASTUS to extraction of information about terrorist incidents [3], extraction of information about joint ventures [2], indexing of legal document s for hypertext, extracting extensive information from military texts (Warbreaker Message Handler) , extraction of information from spoken dialogues [4], and a number of other smaller systems an d pilot applications. We have applied FASTUS to Japanese texts [2, 4] as well as English. Each transducer (or \"phase\") in the series takes the output of the previous phase and map s it into structures that comprise the input to the next phase, or that contain the domain templat e", |
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| "text": "INTRODUCTIO N SRI International participated in the MUC-6 evaluation using the latest version of SRI's FASTUS system [1]. The FASTUS system was originally developed for participation in the MUC-4 evaluatio n [3] in 1992, and the performance of FASTUS in MUC-4 helped demonstrate the viability of finit e state technologies in constrained natural-language understanding tasks. The system has undergon e significant revision since MUC-4, and it is safe to say that the current system does not share a singl e line of code with the original. The fundamental ideas behind FASTUS, however, are retained i n the current system : an architecture consisting of cascaded finite state transducers, each providin g an additional level of analysis of the input, together with merging of the final results. This paper will describe the version of the FASTUS system employed in MUC-6 and highlight the innovations that distinguish it from previous versions described in the literature. SRI used the FASTUS system for each of the MUC-6 tasks : the named entity task, the templateentity task, the coreference task, and the scenario template task. Because a single system, with a single configuration, was used to run all the tasks, and because the first three tasks are in som e sense prerequisites to the fourth, we will focus our attention in this paper on the scenario templat e task. BASIC FASTUS The SRI FASTUS system is based on a series of finite-state transducers that compute the transformation of text from sequences of characters to domain templates. This architecture has proven t o be very flexible, and has been applied with success to a number of different information extractio n tasks in widely varying domains. We have applied FASTUS to extraction of information about terrorist incidents [3], extraction of information about joint ventures [2], indexing of legal document s for hypertext, extracting extensive information from military texts (Warbreaker Message Handler) , extraction of information from spoken dialogues [4], and a number of other smaller systems an d pilot applications. We have applied FASTUS to Japanese texts [2, 4] as well as English. Each transducer (or \"phase\") in the series takes the output of the previous phase and map s it into structures that comprise the input to the next phase, or that contain the domain templat e", |
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| "text": "information that is the output of the extraction process . It is possible to vary the number o f transducers as a parameter of an application, as well as to control precisely how each transducer accepts and produces output . A transducer may handle input by nondeterministically starting at each point in the input stream, or sequentially by determining the final states reachable from th e first point of the input stream, and then restarting the transducer at the end of each successiv e \"best\" analysis . Typically, all FASTUS phases except the final phase follow the latter regimen , and the templates for all the fragments are merged to form the final analysis . Phases also have th e option of passing unanalyzable input to the next phase, or eliminating it from the stream .", |
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| "text": "The MUC-6 system employs the following sequence of transducers :", |
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| "text": "1. Tokenizer. This phase accepts a stream of characters as input, and transforms it into a sequence of tokens. Most English text is tokenized in the same way, so applications that require heavy runtime optimization can replace this phase by one that is coded directl y in the implementation programming language . However, some domains that make unusual demands on tokenization, (i .e. the text contains frequent chemical or mathematical formulas , or names with internal structure, like names for chemical compounds or drugs) may requir e their own tokenizers, and FASTUS makes an excellent rapid-prototyping tool . In Japanese , where tokenization is problematic, we have replaced the tokenization phase by a standar d off-the-shelf segmenter (JUMAN) . The result of the tokenization is to ignore completely th e whitespace in the input text stream . The FASTUS system preserves whitespace informatio n internally to facilitate the analysis of spatially structured objects like tables and outlines, bu t this capability, much exercised in the Warbreaker Message Handler, was of no consequenc e for MUC-6 .", |
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| "text": "2. Multiword Analyzer . This phase is generated automatically by the lexicon to recognize toke n sequences (like \"because of\") that are combined to form single lexical items.", |
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| "text": "The preprocessor is the point at which the application developer can insert a transducer to handle more complex or productive multiword constructs than could be handle d automatically from the lexicon . An example is the transformation of a sequence like \"twent y three\" into a single number, associated with its numeric value .", |
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| "section": "Preprocessor.", |
| "sec_num": "3." |
| }, |
| { |
| "text": "4. Name Recognizer. This phase recognizes word sequences that can be unambiguously identified as names (like \"ABC Corp .\" and \"John Smith\") . It also finds unknown words an d sequences of capitalized words that don't fit other known name patterns, and flags them s o that subsequent phases can determine their type, using broader context .", |
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| "section": "Preprocessor.", |
| "sec_num": "3." |
| }, |
| { |
| "text": "Parser. This phase constructs basic syntactic constituents of English, consisting only of thos e that can be nearly unambiguously constructed from the input using finite-state rules . The output of this phase consists of noun groups (the part of the noun phrase from the determine r through the head noun) and verb groups (the verb together with auxiliaries and adjacent an d intervening adverbs) . Punctuation, prepositions, relative pronouns, and conjunctions are passed through as `particles .'", |
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| "section": "5.", |
| "sec_num": null |
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| "text": "6. Combiner . The combiner produces larger constituents from the output of the parser whe n these can be combined fairly reliably on the basis of local information . Examples are appositives, (\"John Smith, 56, president of Foobarco\"), coordination of same-type entities, an d locative and temporal prepositional phrases .", |
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| "text": "7 . Domain . The final phase recognizes the particular combinations of subjects, verbs, an d objects that are necessary for correctly filling the templates for a given information extractio n task . While the earlier FASTUS phases may have minor domain-dependent parts, they are largely domain independent . Before MUC-6 the domain phase of each FASTUS system was entirely domain dependent, and was rewritten from scratch for each application . In MUC-6 we tested a new idea of a \"domain-independent\" domain phase that can be easily customize d to a new domain . This effort is described below .", |
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| "text": "The basic FASTUS system includes a merger for merging the templates produced by the domai n phase. Merging is essentially a unification operation ; the precise specifications for merging are provided by the system developer when the domain template is defined . The developer specifie s", |
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| "text": "for each slot what type of data is contained in that slot, and for each data type, FASTUS provide s procedures that compare two items of that type and decide whether they are identical or necesaril y distinct, whether one is more or less general than the other or the two are incomparable . Depending on the results of this comparison, the merge instructions specify whether the objects can be merged, or if not, the candidates should be combined as distinct items of a set, or if the merge should b e rejected as inconsistent . The merger makes the assumption that these comparison and merg e decisions are context independent, i .e. it is not necessary to know anything other than the value s of the slots to determine whether they merge . For MUC-6, we found it desirable to allow limite d cross-slot constraints in the form of equality and inequality constraints .", |
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| "section": "5.", |
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| "text": "The development of FASTUS since its introduction in 1992 has been focused primarily on makin g the system easier to use and adapt to new domains . The original system demonstrated in MUC-4 used transition tables that were constructed by hand, and its semantics were embodied solely i n lisp code associated with the virtual machine states . For MUC-5, we had developed a system that allowed the system developer to encode automata with a graphical user interface that constructe d the transition tables . Subsequent to MUC-5 we developed a specification language (called FAST-SPEC) that allows the developer to write regular productions, that are translated automatically into finite state machines by an optimizing compiler .", |
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| "section": "FASTUS FOR MUC-6", |
| "sec_num": null |
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| "text": "This last step greatly facilitated the ability to port FASTUS to new domains quickly. Th e shortcoming remained, however, that writing FASTSPEC rules was not something that one coul d reasonably expect an analyst to do in response to an information extraction need . If information extraction systems are going to be used in a wide variety of applications, it will ultimately b e necessary for the end users to be able to customize the systems themselves in a relatively shor t time .", |
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| "section": "FASTUS FOR MUC-6", |
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| "text": "Customizing an extraction system to a domain has always been a long and tedious process . One must determine all the ways in which the target information is expressed in a given corpus , and then think of all the plausible variants of those ways, so that appropriate regular patterns ca n be written. Because computational linguists have been developing systems for a long time tha t employ grammars that capture the relevant linguistic generalizations, one might be led to believ e that systems that are based on linguistically-motivated English grammars would be much easier t o adapt to a new domain .", |
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| "text": "It has, however, been the experience at past MUC evaluations that systems based on genera l grammars have not performed as well as those that have been customized in a more applicationdependent manner . The reasons for this are more practical than theoretical . General grammars of English, by virtue of being general, are also highly ambiguous . One consequence of this ambiguity i s that a relatively long processing time is required for each sentence ; this implies, in turn, a relatively long develop-test-debug cycle. Moreover, these systems have proved rather brittle when faced wit h the multitude of problems that arise when confronted by real-world text . (Lack of robustness may not be inherent in the approach, and much of the current work in corpus-based statistical model s is an attempt to overcome this problem).", |
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| "text": "One might naturally wonder whether one can have the advantages of both worlds : tightl y defined, mostly unambiguous patterns that cover precisely the ways the target information is expressed, and a way of capturing the linguistic generalizations that would make it unnecessary fo r an analyst to enumerate all the possible ways of expressing it . We feel that the FASTUS syste m developed for MUC-6 represents a major step toward achieving this synthesis .", |
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| "text": "In the current FASTUS system, we attempt to localize the domain-dependence of the rules t o the maximum extent possible . To this end, the FASTPEC rules of the domain phase have bee n divided into domain-dependent and domain-independent portions . The domain-independent par t of the domain-phase consists of a number of rules that one might characterize as parameterize d macros . The rules cover various syntactic constructs at a relatively coarse granularity, the objective being to construct the appropriate predicate-argument relations for verbs that behave accordin g to that pattern . The domain-dependent rules comprise the clusters of parameters that must b e instantiated by the `macros' to produce the actual rules . These domain-dependent rules specify precisely which verbs carry the domain-relevant information, and specify the domain-dependent restrictions on the arguments, as well as the semantics for the rule .", |
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| "text": "An example of a typical macro rule is the rule called ActiveBase : This rule describes the basic subject-verb-object pattern of a simple active-voice declarativ e sentence with a transitive verb . The EVENT-ADJUNCT non-terminal parses locative and tempora l adjuncts (as well as absorbing otherwise unknown constituents) . The next optional constituent i s the subject noun phrase, which optionally skips any complements that may be present, followed b y an active verb, an optional object, and up to three prepositional arguments, optionally intersperse d with temporal and locative adjuncts. The alert reader will notice that the only required element in this pattern is the verb-in analyzing a typical sentence, each pattern will be instantiated multipl e times as FASTUS nondeterministically ignores or recognizes the various arguments . The preferre d analysis is, of course, the one that is the most complete .", |
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| "section": "FASTUS FOR MUC-6", |
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| { |
| "text": "EVENT-PHRASE --> EVENT-ADJUNCT* (NG[??subj] ({COMPL I COMPL1}) ) VG[Active=T,", |
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| "section": "FASTUS FOR MUC-6", |
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| "text": "The tokens beginning with \"??\" in the above example are parameters that are specified by th e domain-specific rules when the macro is expanded . Thus, this pattern applies only to noun group s meeting the \"??sub j\" constraints, and to verbs meeting the \"??head\" restrictions, etc .", |
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| "section": "FASTUS FOR MUC-6", |
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| "text": "Currently, domain-specific rules are centered around verbs . In a typical information extraction task, one is interested in events and relationships holding among entities, and these are usually specified by verbs . Verbs, of course, have corresponding nominalizations, so the macros shoul d automatically instantiate nominalization patterns as well . Unfortunately, the current FASTU S lexicon is not rich enough reliably to make the connection between verbs and their correspondin g nominalizations, so the FASTUS system employed for the MUC-6 evaluation did not recognize any nominalized events (like \"resignation\" or \"promotion\") . This is an example of a large gap that i s easy to close.", |
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| "text": "The success of this general approach depends heavily on two prerequisites : reliable coreference resolution and a well-developed combiner phase . The coreference module is necessary because i t relieves the developer of the domain phase rules of the burden of anticipating all the variations tha t would result from pronominal and definite reference . Otherwise the developer must see to it tha t every rule that involves a company as subject also applies to \"it,\" when it refers to a company, a s well as to \"the company,\" \"the concern,\", etc . The FASTUS coreference module resolves pronouns , reflexives, definites, and some bare nominal temporal expressions, with simple algorithms . (There is a separate Alias Recognition module that also contributes to the overall coreference output . ) The entity associated with an anaphor gets merged with the first consistent entity found while traversing an ordered list of candidate phrases, each of which is associated with a set of entities . Different types of anaphors call for slightly different candidate phrase ordering and consistenc y checking algorithms . Our focus was on coreference of phrases that referred to individuals, no t types, for it is individual coreference that is needed in most information extraction tasks . Type coreference is both theoretically and practically more difficult, as evidenced by the difficulty o f reliable bare-nominal resolution, and its utility in information extraction tasks is unclear . Areas of future extensions are intrasentential coreference based on sentence patterns and limited plausibility inferences based on described events .", |
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| "text": "The combiner has the responsibility of correctly analyzing appositives and noun-phrase conjunction . This makes it possible for the domain phase to skip complements correctly . If all thi s work is done, then the specification of domain-specific rules can be a surprisingly simple task .", |
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| "section": "FASTUS FOR MUC-6", |
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| "text": "This system of compile-time transformations allowed us to cover with 12 macro rules and 1 5 domain-dependent rules what would otherwise require approximately one hundred patterns, wer e the patterns to be written out explicitly. (Not every macro rule applies to every domain-dependen t rule.) The domain phase for MUC-6 was developed in less than one person-day .", |
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| "section": "FASTUS FOR MUC-6", |
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| "text": "The set of FASTSPEC grammar rules resulting from the application of the domain-independen t macros to the domain-dependent parameters are very close to those that a developer would have written, had he or she been encoding them directly. Thus, the macro rules facility preserves th e ability to write patterns that are tightly constrained to fit the particular relevant sentences of th e domain, but with the additional advantage of automatically generating all of the possible linguisti c variations in an error-free manner . A developer need no longer lament having failed to includ e a `passive' variant of a particular pattern simply because no instance occurred in the trainin g corpus . Also, the information specified by the domain-dependent rules is relatively straightforwar d to provide, (although currently obscured by a rather opaque syntax) so that with the help of a suitable user interface, it is easy to imagine an analyst supplying the system with the informatio n needed to customize it to a new extraction task . Developing such tools is one of our next priorities .", |
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| "section": "FASTUS FOR MUC-6", |
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| "text": "FASTUS achieved an outstanding result of F 94 (Recall 92, Precision 96) on the named entit y recognition task . The scores for the Template Entity task were somewhat lower F 75 .0 (Recall 74 , Precision 76) . This is to be expected, because some of the named entities, such as percentages, ar e very easy to extract reliably, and some of the fields in the template entity task (e .g. descriptors ) are extremly difficult to extract reliably. The system consistently made certain errors in nam e recognition , and because these culprits popped up often, they had a substantial impact on th e score .", |
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| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
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| "text": "\u2022 Although there were numerous instances in the test corpus in which \"White House\" was use d", |
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| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
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| "text": "to refer straightforwardly to the building, the system always classified it as a government organization .", |
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| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
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| "text": "\u2022 Company names that are identical to person names are a frequent source of error . The surname is sometimes categorized as an alias for the person and sometimes as an alias for th e company, depending on where the surname appears relative to the person name or compan y name in the text .", |
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| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
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| "text": "\u2022 Newspapers are to be classified as companies only when the name is intended to refer to th e publishing company rather than the periodical . We currently have no overall strategy fo r distinguishing these cases, although we do pick them up as companies if they are involved i n succession events in the scenario template task .", |
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| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
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| "text": "\u2022 Location names were to be treated as government entities when the intended referent of th e name was the government . We made no attempt to do this correctly.", |
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| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 When two named entities were combined in a phrase like an appositive that is recognize d by the combiner, one of the entites would frequently be lost . For example, \"John Smith, a Johnson & Johnson vice president,\" would lose Johnson & Johnson . This was due to some remaining bugs in the combiner grammar .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
| }, |
| { |
| "text": "FASTUS achieved one of the better results in the coreference task, with Recall of 59 an d Precision of 72 .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
| }, |
| { |
| "text": "In the scenario template task, SRI's FASTUS system achieved a score of F 51 .0 (Recall 44 , Precision 61) . The details of the scenario template task are discussed in the following section .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
| }, |
| { |
| "text": "SRI has been involved in information extraction research for over ten years . As mentione d earlier, the FASTUS System has been under development for a little over three years. SRI undertook a substantial effort prior to the MUC-6 evaluation to clean up all of the domain-independen t processing phases, so the domain-independent macro rules could be tested and validated . This effort lasted well into the development period for the MUC-6 evaluation . In fact, we were not abl e to do a scoreable run of the development training corpus until September 22-two weeks befor e the test. During this period we were able to quickly bring the system from an F-measure of 32 .2 to 55.3 the day before the test . Nearly all the development effort was focused on the combine r phase and on merging and coreference . As noted above, the total amount of time spent on domai n patterns was less than a day . Examining the results of the test leads us to believe that many of the problems the system encountered represent not conceptual difficulties but easily fillable gaps , such as the nominalization problem referred to above, or missing domain-relevant lexical feature s on important words, that would disappear with a short period of additional development .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
| }, |
| { |
| "text": "This experience also supports the view that customization of FASTUS to a new domain i s relatively easy and thus gives us reason for a good deal of optimisim about the future for practical applications of information extraction technology.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "OVERALL PERFORMANCE ON MUC-6 TEST S", |
| "sec_num": null |
| }, |
| { |
| "text": "The difficulty of building an extraction system is determined to a significant extent by the desig n of the templates to be filled . Ideally, the structure of the templates will correspond in a systemati c way to the linguistic structures through which the relevant information is typically expressed i n natural language . Unfortunately this ideal is rarely met .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DISCUSSION OF THE EXAMPL E", |
| "sec_num": null |
| }, |
| { |
| "text": "The MUC-6 template for the scenario template task presented certain problems . In particular there was a lack of fit between the conceptualization of succession events embodied in the templat e and the typical expression of the corresponding events in language . For example, it is often th e case that a single event report (e .g. \"John Smith left Microsoft to head a new subsidiary a t Apple\") corresponds to multiple succession events . Conversely, it is (even more) typical to have a single succession event expressed by multiple sentences (events-reports), often far removed fro m one another . Also, static information (e .g. \"John Smith has been chairman for the last five years .\" ) is often essential to filling the final template, although the succession event structure provides n o way of representing this static information .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DISCUSSION OF THE EXAMPL E", |
| "sec_num": null |
| }, |
| { |
| "text": "We feel that the proper template design, or ontology, is essential for the rapid development of a n information extraction application . For this reason we developed our own internal representation o f the domain that corresponded more closely with the ways the information is typically expressed i n the texts . A post processor was written to generate the official MUC-6 templates from this interna l representation .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The Representation of States and Transition s", |
| "sec_num": null |
| }, |
| { |
| "text": "We felt that a more appropriate representation of the domain involved two kinds of structures : states and transitions . A state consists of the association among a person, an organization, and a position at a given point in time . A transition is a ternary relation between states and reasons , associating a start state and and end state with a transition reason . In what follows, we will us e \"position\" to refer to position-organization pairs .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The Representation of States and Transition s", |
| "sec_num": null |
| }, |
| { |
| "text": "The system recognizes two kinds of transitions associated with a succession event : a perso n pivot, which is a transition in which a start state involving a person and a position is related a state involving the same person but a different position, and a position pivot (which is similar to a succession event), which is a transition in which the start and end states involve a single positio n and two different people . If a sentence directly implies one of these transitions, then transitions o f the other type ('shadow' transitions) are also implied . For example, given the sentence \"John Smit h resigned as executive vice president of Microsoft\" the system represents the content of the sentenc e as a transition involving the state \"John Smith, executive vice president, Microsoft\" to \"Joh n Smith, some other position, some other company .\" The system then also generates the implie d position pivot, namely the transition from \"John Smith, executive vice president, Microsoft\" t o \"Some person, not John smith, executive vice president, Microsoft .\"", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "The Representation of States and Transition s", |
| "sec_num": null |
| }, |
| { |
| "text": "The shadow transitions provide a locus for merging of other states and transitions that may b e mentioned in the text . For example, if the next sentence were \"Joe Schmoe will assume the pos t of vice president next month,\" it would produce a shadow position pivot that would merge wit h the shadow position pivot from the previous sentence. States that are not otherwise associate d with transitions can be merged with transitions . If the next sentence were \"Joe Schmoe is the new executive vice president,\" this would also merge with the end state of the shadow position pivo t generated by the previous sentence .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The Representation of States and Transition s", |
| "sec_num": null |
| }, |
| { |
| "text": "We decided to augment the FASTUS merger, described in Section 2 above, to handle equalit y and inequality constraints among slots . Position pivots and person pivots come with pre-specified constraints among their slots stating which elements of the participating states have to be the sam e and which must be different . The merger will refuse to merge two templates for which the equalit y and inequality constraints are not satisfied by the resulting merge . This feature, preventing sparsel y instantiated templates from overmerging, has now been incorporated into the general FASTU S merger.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Mergin g", |
| "sec_num": null |
| }, |
| { |
| "text": "The official score for FASTUS on the walkthrough message was Recall 50, Precision 60 . FASTU S did about as well on this message as on the test as a whole, which implies that this was a fairl y typical message, at least as far as the system's processing was concerned .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "The Walkthrough Exampl e", |
| "sec_num": null |
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| "text": "It is thus quite instructive (even to us) to examine the system's response . FASTUS missed the transition event regarding the chairmanship of McCann-Erickson . The key sentence, in paragraph 2, where this was introduced was misanalyzed due to a simple bug i n the lexicon . The succession event involving Kim was missed for the simple reason that the ver b \"hire\" was never considered as a domain-relevant verb . There is no conceptual problem here-thi s is merely a consequence of the short development time available . Adding a sub ject-verb-objec t pattern \"Company hires or recruits person from company as position\" and one more small gap i n the system's coverage is filled .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "The Walkthrough Exampl e", |
| "sec_num": null |
| }, |
| { |
| "text": "What was more disturbing was the second overgenerated succession event found by FASTUS , which was a succession involving Dooner out and Alan Gottesman in as president of Paine Webber . Inspection of the text reveals that Alan Gottesman was mentioned as an analyst with Paine Webber , and was not involved in any succession events . Closer analysis reveals precisely what happened : one sentence in the text is \"There are no immediate plans to replace Mr . Dooner as president . \" The subject of the sentence did not receive a domain analysis, bug the verb phrase \"replace Mr . Dooner as president\" did receive an analysis and produced a partially instantiated position pivo t transition with Dooner as president of something being replaced by somebody else as presiden t of something . The mention of Alan Gottesman as an analyst at Paine Webber produced a stat e (not associated with any transition) consisting simply of Alan Gottesman and Paine Webber (sinc e the position \"analyst\" was not a high corporate officer, it was simply ignored, and the position i n the template left uninstantiated) . When merging took place, this state merged with the sparsely instantiated end state of the position pivot, filling out the overgenerated transition and leadin g eventually to the incorrect succession event .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "The Walkthrough Exampl e", |
| "sec_num": null |
| }, |
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| "text": "We were dismayed to discover what appeared to be a grevious but previously undetected bug : sparsely instantiated states and transitions were being allowed to merge, producing many spuriou s results . This bug was fixed by establishing some minimal instantiation requirements for state t o transition merges, and we reran the test and rescored the results . We discovered that our score with the `bug' fixed was F 47 .6, (Recall 36, Precision 69) . This bug had purchased us an increas e of nearly 4 points in F measure .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "The Walkthrough Exampl e", |
| "sec_num": null |
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| "text": "While it is tempting at this point to relabel the `bug' as a `feature' and consider the matte r no further, there is actually a rather interesting story to be told as to why our performance wa s helped so much by this bug, a story that suggests interesting lines for further investigation .", |
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| { |
| "text": "Hardly anyone has attempted to develop a high-recall low-precision extraction system . Part of the problem is that it is far from clear how to go about doing it . Typically, extraction systems ar e built by implementing some likely domain-relevant patterns that signal important information in the text, and then examining ever more texts to find the ever less frequent patterns that signal tas k relevance . This procedure naturally approaches the problem from the low-recall, high precisio n side . The first patterns that come to mind are likely to be the most reliable . As you add more an d more of the rare ones, eventually precision declines as recall creeps upward .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "High Recall, Low Precision Extraction", |
| "sec_num": null |
| }, |
| { |
| "text": "But, what if one wanted to approach the problem from the other angle? The basic idea woul d be the following : posit every entity of the right type as a candidate for participation in one of th e events/relationships of interest, merge to produce more fully instantianted events/relationships an d then filter according to some application-specific criteria . It is plausible to suppose that one woul d start with fairly high recall and gradually, by developing better filter criteria, one would eliminat e most of the clearly irrelevant hypotheses, while eliminating few of the relevant ones . This is a quite reasonable approach for certain extraction tasks, even those tasks for which high recall and low precision is not an acceptable tradeoff. Such tasks are characterized by th e following features: (1) entities in the domain have easily determined types and (2) the template s are structured so that there is only one or a very small number of possible slots that an entit y of a given type can fill and only entities of a given type can fill those slots . The microelectronic s domain of the MUC-5 evaluation [6] was a good example of a domain with these characteristics , and techniques similar to these were successfully applied by at least one system in that evaluatio n [5] . Our own experience in working in the labor negotiation domain of the MUC-6 dry run ha s suggested that that domain was also reasonable to approach from this standpoint .", |
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| "start": 1118, |
| "end": 1121, |
| "text": "[6]", |
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| }, |
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| "end": 1286, |
| "text": "[5]", |
| "ref_id": "BIBREF4" |
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| "section": "High Recall, Low Precision Extraction", |
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| { |
| "text": "We attempted to develop a system that approached the succession task in this manner . We called this approach the `atomistic' approach or the `one rule' approach, because it was based o n finding distinct atoms of relevant information and it was implemented by a single domain rule i n FASTUS . This single rule would look for any PERSON, COMPANY or POSITION in the text , and hypothesize a transition event involving that entity . These typically very partial transition s would be merged and finally a post processor would be invoked to filter the resulting hypothesize d transitions according to various experimental criteria .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "High Recall, Low Precision Extraction", |
| "sec_num": null |
| }, |
| { |
| "text": "After experimenting with this approach for a while, it seemed to us that it would be difficul t to raise the F-score beyond the low 40s. The regular ('molecular') FASTUS approach with th e macro-expanded domain rules was already doing as well in tests and it appeared to have mor e promise. We began devoting all our efforts to it .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "High Recall, Low Precision Extraction", |
| "sec_num": null |
| }, |
| { |
| "text": "We did realize that the two approaches raised interesting questions, however . In particular, if one has results from both high-recall and high precision systems, can these be combined in som e way to produce a result that would be better than either system taken on its own? The answe r was by no means obvious, and in the end we put aside both the atomic approach and any attemp t to combine the results .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "High Recall, Low Precision Extraction", |
| "sec_num": null |
| }, |
| { |
| "text": "One way to view the bug we discovered in our system is that it accomplishes just that : the bug embodied, quite accidentally, a not unreasonable strategy for selectively adding informatio n to the result, even though the domain phase did not detect a transition involving the entity . Although there was not enough information to actually determine what states the transition applie d to, FASTUS was extracting just enough information from the text to conclude that there was a transition. The system then picked some state to instantiate the transition, and this state was both (1) mentioned in general textual proximity to the transition, and (2) not involved in an y other known transition event . Although this occasionally produces ridiculous hypotheses, it i s frequently correct ; transition events are often mentioned in texts in clusters, and the proximit y heuristic works well .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "High Recall, Low Precision Extraction", |
| "sec_num": null |
| }, |
| { |
| "text": "We were generally pleased with the results of FASTUS in this evaluation . Our name recognition was close to the best of the among the participating systems and is approaching the practica l maximum performance level for this task . Our coreference module performed the best among all the participants . More important, the module played an important role in the scenario template system, and plays an important role in enabling the system to be easily customized to new domains .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ASSESSMENT OF THE RESULT S", |
| "sec_num": null |
| }, |
| { |
| "text": "The results in the scenario template evaluation were acceptable and analysis of the particular problems encountered reveals that there are still large gains in performance to be had by simple , straightforward hill climbing on training texts .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ASSESSMENT OF THE RESULT S", |
| "sec_num": null |
| }, |
| { |
| "text": "One of the most promising results of our MUC-6 preparation effort is that we have implemente d a complete extraction system using the macro rules that we proposed a year ago . It allows a significant localization of the domain dependence of the system and this is an essential step towar d enabling customization of the system by its end users .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ASSESSMENT OF THE RESULT S", |
| "sec_num": null |
| }, |
| { |
| "text": "As we mentioned in this article, the amount of time spent analyzing and implementing domai n patterns for this evaluation was very minimal-a little more than half a day . Given that most of the effort required to develop the domain independent parts of the system to support the macr o rule approach has already been done, if we were to repeat a similar domain task, we suspect tha t much higher performance could be achieved with much less effort .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ASSESSMENT OF THE RESULT S", |
| "sec_num": null |
| }, |
| { |
| "text": "How successful were we in isloating domain dependence? There were still a few parts of th e larger FASTUS system that had to be modified in response to this task . The combiner rule fo r recognizing appositives had to be modified, because of the frequency of patterns like \"John Smith , 56, president of Foobarco, ...\" Phrases representing positions were marked, but this marking ca n be derived from features on the head noun . We modified the FASTUS merger to include the equality and inequality constraints, but, as suggested above, this requirement is likely to be usefu l in implementing other domains as well, and will be retained as part of our basic system .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ASSESSMENT OF THE RESULT S", |
| "sec_num": null |
| }, |
| { |
| "text": "Our experience from MUC-6 suggests two promising areas for further work . The first area is that of tool development to facilitate the customization of the system by analysts. We have developed the underlying infrastructure required to make this possibility a reality, and we now have the capabilit y to begin experimenting with strategies for specifying patterns, and learning patterns from examples .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FUTURE DIRECTION S", |
| "sec_num": null |
| }, |
| { |
| "text": "The other area of research suggested by our serendipitous bug is to investigate more principle d means for combining the results of low-recall high-precision analysis, and high-recall low-precisio n analysis . Our experience in this evaluation suggests that there may be strategies based on partia l information, and textual proximity that yield promising results, particularly for applications i n which some sacrifice of precision for increased recall is reasonable .", |
| "cite_spans": [], |
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| "section": "FUTURE DIRECTION S", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "This research was supported by the Advanced Research Projects Agency under contract N66001 -94-C-6044 with NCCOSC, and contract 94-F-1577-00-000 with ORD .", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "ACKNOWLEDGEMENT S", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
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| "raw_text": "Appelt, D . et al., FASTUS: A Finite-State Processor for Information Extraction from Real - World Text, Proceedings of the 13th International Joint Conference on Artificial Intelligenc e (IJCAI-93), August, 1993, pp . 1172-1178 .", |
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| "text": "The key postulates three succession events for the text : James out, Dooner in as CEO o f McCann-Erickson, James out, Dooner in as chairman of McCann-Erickson, and Kim in as vice chairman of McCann-Erickson." |
| } |
| } |
| } |
| } |
