| { | |
| "paper_id": "H93-1026", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:30:39.368319Z" | |
| }, | |
| "title": "FASTUS: A System for Extracting Information from Text*", | |
| "authors": [ | |
| { | |
| "first": "Jerry", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Hobbs", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "addrLine": "333 Ra.venswood Avenue Menlo Park, C", | |
| "postCode": "94025" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Douglas", | |
| "middle": [], | |
| "last": "Appelt", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "addrLine": "333 Ra.venswood Avenue Menlo Park, C", | |
| "postCode": "94025" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Bear", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "addrLine": "333 Ra.venswood Avenue Menlo Park, C", | |
| "postCode": "94025" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Israel", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "addrLine": "333 Ra.venswood Avenue Menlo Park, C", | |
| "postCode": "94025" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Megumi", | |
| "middle": [], | |
| "last": "Kameyalna", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "addrLine": "333 Ra.venswood Avenue Menlo Park, C", | |
| "postCode": "94025" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mabry", | |
| "middle": [], | |
| "last": "Tyson", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "SRI International", | |
| "location": { | |
| "addrLine": "333 Ra.venswood Avenue Menlo Park, C", | |
| "postCode": "94025" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "", | |
| "pdf_parse": { | |
| "paper_id": "H93-1026", | |
| "_pdf_hash": "", | |
| "abstract": [], | |
| "body_text": [ | |
| { | |
| "text": "FAS'rUS is a (slightly permuted) acronym for Finite State Automaton Text Understanding System. It is a. system [br extracting information fi'om free text in English (Japanese is under development), for entry into a database, and potentially for other apl)lications. It works essentially as a set of cascaded, nondeterministic finite state automata.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "FASTUS is rnost appropriate for inform.ation e~:lraclion tasks, rather than fldl text understanding. That is, it. is most effective for text-scanning tasks where", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Only a fi'actiou of the text is relevant.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 There is a. pre-defined, relatively simple, rigid target representation that the information is mappe(I into.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 The subtle nuances of meaning a,nd the writer's goals in writing the text are of no interest.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The opera.tion of FASTUS is comprised of four steps.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE STRUCTURE OF THE MUC-4 FASTUS SYSTEM", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "l. Triggering: Sentences are scanned for key words to determine whether they should be processed flirt.her.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE STRUCTURE OF THE MUC-4 FASTUS SYSTEM", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "2. Recognizing Phrases: Sentences are segmented into noun groups, verb groups, and particles.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE STRUCTURE OF THE MUC-4 FASTUS SYSTEM", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "3. Recognizing Patterns: The sequence of phrases produced in Step 2 is scanned for patterns of interest, and when they are found, corresponding \"incident structures\" axe built. Many systems have been built to do pattern matching on strings of words. One crucial innovation in the FASTUS system has been separating that process into the two steps of recognizing phrases and recognizing patterns. Phrases can be recognized reliably with purely syntactic information, and they provide precisely the elements that are required for stating the patterns of interest. The system is implemented in CommonLisp and rmls on both Sun and Symbolics machines.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE STRUCTURE OF THE MUC-4 FASTUS SYSTEM", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The task in the MUC-3 and MUC,-4 (Message Understanding Conference) evaluations of text processing systems was to scan news reports and extract intbrmation about terrorist incidents, in particular, who did what to whom. Tim following sentence occurred in one rel)ort:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "AN EXAMPLE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Salvadoran President-elect Alfredo Cristiani condemned the terrorist killing of Attorney General Roberto Garcia Alvarado and accused the Farabundo Marti National Liberation Front (FMLN) of the crime.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "AN EXAMPLE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This sentence is triggered because it has a nmnber of key words, including \"terrorist\", \"killing\", and \"FMLN\".", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Triggering:", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Step 2 segments tim sentence into the following phrases: The phrases that are recognized are nantes, the noun group, or the noun phrase up t.hrough tile head noun, the verb group, or the verb together with its a.uxilliaries and any trapped adverbs, and various particles, including prepositions, conjunctions, relatiw~ pronouns, the word \"ago\", and tile word \"'that\" which is treated Sl)ecially because of the ambiguities it gives rise to. Essentially the full complexity of English noun groups and wq'b groul.)S is accommodated. This phase of the processing gives very reliable resultsbetter than 96% accuracy on the data we haxe examined. 3 with the other descriptions for that name. A precise description can be merged with a vague description, such as \"person\", with the precise description as the result. Two precise descriptions can be merged if they a.re sen)antically compatible. The descriptions \"prieslY and \"Jesuit\" are compatible, while \"priest\" and \"peasant\" are not. When precise descriptions are merged, the longest string is taken as the result. If merging is inlpossible, both noun groups are listed in tile slot.", | |
| "cite_spans": [ | |
| { | |
| "start": 641, | |
| "end": 642, | |
| "text": "3", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Recognizing Phrases:", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "Pattern-matching approaches have often been tried in the past, without much success. We believe that our success was due to two key ideas. The frst, as stated above, is the use of cascaded finite-state automata, dividing the task at the noun group and verb group level. The second is our approach to skipping over complements. One significant problem in pattern-matching approaches is linking up arguments with their predicates when they are distant in the sentence, for example, linking up the subject noun group with the main verb when the subject has a number of nominal complements. One technique that has been tried is to skip over up to some umnber of words, say, five, in looking for tile subject's verb. One trouble with this is that there are often more t.han five words in tim subject's nominal complement. Another trouble is that in a. sentence like", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SKIPPING COMPLEMENTS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The police reported that terrorists bombed the Parliament today.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SKIPPING COMPLEMENTS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "this teclmique would find \"the police\" as the subject of \"bombed\". Our approach is to implement knowledge of the grammar of nominal complements directly into the finite-state pattern recognizer. The material between the end of the subject noun group and the beginning of the main verb group nmst be read over. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SKIPPING COMPLEMENTS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We are crrrrenl.ly ~'xl.ei/ding the I;'ASTU,q sysl.ein hi three ways:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECENT EXTENSIONS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 We are develolYiug a. corlvorrieill, ilit(,r'fa.ce t]la, t will a.llow risers I,o oh'fine i)alJ,er'llS iriore easily.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECENT EXTENSIONS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 We axe irnphmwnt,in~ a Japa.nese la.nguage version of FASTLiS.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECENT EXTENSIONS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 \\'V(' are apl)lying i,he syst,em to a, new domainexl.ra.ctiilg i[,tbrnla.tion a.bout joint velltrlres fl'Ollr news articles.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECENT EXTENSIONS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The last of these will be the subject of our M U(:-5 paper. The other l.wo awe descri/)ed hero.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "RECENT EXTENSIONS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The original version o[' li'A,lgT[JS has been augniented with a convenienl, graphical user interface for iniplenlellt, illg O1\" extending aJI application, eniployillg Sill's Grasper systenr (Karl) el. a.l., 1993). We expect this to speed up developrirerrt time for a new application by a factor of three or four. Moreover, whereas hefore riow only a systenl dew4oper could inlpleinent a new application, now virtrrally a.nyoue should I)e able to. In a specification interface tbr FASTUS, there needs to be convenient means for performing four tasks:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE INTERFACE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "1. Defining ta.rget strtr('tlrres.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE INTERFACE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "2. Defining word classes.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE INTERFACE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "3. Defining sta.te l, ra.risitiorrs.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "THE INTERFACE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "ViSe have dolle nothing yet irl the firsl, two areas, since e.veryone currently working with the syst(~nl is tltlent ill Lisp. Target structures are defined with defstruct, word classes with deDa.r. As we acquire users who are not programmers, it will be straighth)rward to inil)lenlent convenient means for these tasks. The Grasper-based graphical interface provides a convenient inemls for creating, exaulining, editing, and destroying nodes arid links in the graphs representing the finite-state automata. Each link is labelled with the tokens that cause that transition to take place. Nodes have associated with them sequences of instructions that are executed when that node is reached. These instructions typically fill slots in the target strlrctures, and they ~can be conditionalized on what link the node was reached from, allowing greater economy in the finite-state machiries.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Defining nierge coirditioris.", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "In addition, the interface allows the graphs a,t each level to be modularized in whatever fashion the user desires, so that at any given tin]e, the user can focus on only a small portion of the total graph. There are also convenient means for saving and compiling the graphs afl.er changes have been made. Perhaps the hardest problem in the inforn]ation extra.ction task is defiifing when two target structures can be merged. This is, after a.ll, the coreference l)rol)h'nr in dis-('Oilr'SO, well-knowrl to I)e \"al-eomlilete\". W'e have develol)ed a kiird of a.lgebra on l,he l,a.rgel, sl, ructures. 'Hie rrser can define abstract data types, inchiding ntlniber rallges, date ranges, locations, and strings. Comparison opera.lions can then be defined for each of these data. l.ypes, returning vahies of Equal, Snbstnnes, Inconsistent, and hlcorupa.rable. (]onlbina.tion operations ca.n also be defined. For exainple, the cornbination of two uunil)er or date ranges is the nlore restrictive range. For striligs, the conll)ination depends on the semantic categories of the heads of the strings. If one is more specific than the other, the more sl)ecific term is the resu It. of the combhmt,ion. There are t.hree types of actions that be l)erformed after doing a comparison. The items can be merged or c.ombined. If they are incomparable and if the slot. in t.he target struct,ure admits eonlpound entries, die two call simply be added together. Or the unification of the l.wo items can be rqjected.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Defining nierge coirditioris.", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "ad hoe. FASTUS has been restrtictured somewhat a.s well since MUC-4. A Tokenizer Phase has been added, its input consists of ascii characters and it output is tokens, usually words, numerals, and punctuation lnarks. This phase gives the user control over the lowest level of input,, so that special rules can be encoded for abbreviations, numbers with radix other than 10, and other such phe-nOlllena. The illOSt conlnlon tokenizations are, of course, ah'eady iniplenmnted.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "This algebra of target structures gives us a very clean treatment of what in the MUC-4 systenl was often very", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A Preprocessor Phase has also been added. This incof pora.tes t,he nmltiword handling that. was done in t.he Phrase R.ecognition phase of the first, version of FAS-TUS. It also allows the user to customize automata lot dealing, for example, with names that have a different given-name falnily-nanae order and with names of nonhuman entities that have internal structure significant to /.he donm.in, such a.s company names. The treatment of appositives, conjunct,ions, and \"oF' prepositional phrases was originally done in the Pattern Recognition phase. This has now been separated out into a Combining Pha.se for a. treat.nlent tha.t is nlore perspicuous and hence more conw?nient for the user.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "This algebra of target structures gives us a very clean treatment of what in the MUC-4 systenl was often very", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We are also developing a Japanese version of Ia)~SJ'US.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JAPANESE FASTUS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The initial application is for extracting a, summary of spoken diMogues, inpu{, in R,omari characters, in the domain of conDrenee room reservatiolls. Smmnarizing goal-oriented dialogues can be achieved by filling a predefined sumnlary tenq)late, and a.ny digressions in the dialogue content can 1)e ignored. Sunnnarization is i, hou an exalnple of expectatiorl-driven inforlila.tioli extracl,ion performed by FAS'FUS.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JAPANESE FASTUS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Despite the dissiniila.ril.y bet.weon t.he English and .}al)a.nese lallglla.ges, t.ho Basic FASTUS a, rchit.o('l, llrO COil-sis ing of [bur phases can be a.l)plh~d to the processing of .la.i)anese. The phrase recognition phase (phase II) recognizes noun groups, verb groups, and parlicles. The phrase coral)tirol, ion phase (phase II1) recognizes the \"N(~ no NG\" l)hra.ses (similar to the English \"of\" phrases) and N(I conjunctions that a.re of interest, to the giwm domain. The incident recognition phase (phase IV) recognizes those ut, tera.nce patt,erns that conl,ain key inrorma.l,ion releva.nt l.o the sumnmry template. Because the inl)ut, is Sl)ont, aueous dialogues rather than writt.en news reports, we will have a dialogue managing module a.fter the. incident recognition phase in order to combine intbrma.tion contained in successive dialogue turns---for instance, question-answer pairs and requestconfirmation pairs. We have implemented phases ]l and Ill, and phase IV will be in place shortly.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JAPANESE FASTUS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The main complexity of summarization in this room reservation domain is in the use of tempora.l expressions and in the dynamics of negotiation between the two speakers. Written news report,s typically report past ewnlts whose resulting states are already known. Spoken dialogues, however, progress through a sequence of negotiations where the speakers express their desires, possi-Ifilit.ies, iml)ossibilities, concessions, accel)tances, a.nd so [ortrh. This is a considerable challenge to the structlu:e merging routine of FAS'I'IIS. For i.he M U( '.-5 particil)ation, the Jal)anese FASTUS system will be extended for the new domain of joint ventures and the new inl)ut type of written news reports in J apa.neso charact.ers.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "JAPANESE FASTUS", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The advantages of the FAS'I'IIJS system are as [;allows:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 It, is concept.ually simple. It is a set of cascaded fin ite-state a.utomat.a.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 The basic system is relatively small, Mthough the dict.ionary and other lists are potentially very large.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 It is effective. It. was among the top few systems in I.he MUC-4 evaluation.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 It has very fast run time. The average time for analyzing one message is less than 10 seconds. This is nearly a.n order of magnitude faster than compara-I)le sysl.ems.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 In part I)ecause of the fast nm time, it has a very ra.sL dewqopment time. This is also true because the system provides a wiry direct link between the texts being analyzed and the da.l.a being extracted.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We I)eli,'w\" thai. the le:'\\STUS technology can achieve a level or (i0(~, r,'call and 60% precision oi;i hlforn-iation exl.racl.ion l.asks Ilk,' thai. or M U(.:-~I. tlunian coders do not agree on flus task nlore than 80% of the i,hue, tlenc(', a systeln working ten tinles as fast as [lllllla.ns do ('all achieve 75% of hulnan perforrnau('e. We beliew\" that conabining this system with a good user interface couhl increase the productivity of analysts by a factor of' live or ten in this task. This of course raises the quest, iou about the final :25%. ttow call we achieve that? We believe this will not be achiew~d until we niake substantia.l progress on the long-term problem of hill text undersla.uding. This callnot hai)peri until there is a long-terrn connnitnienl, that nlakes resources available for innovative research on 1,he l)roblem, research tiiat will ahllOSt surely not produce striking results on large bodies of text in the near hlture. Absent such an environment, our inmiediate plans are to spend about two months bringing our MUC-5 system to and beyond the level of our MUC-4 systeni, and then to explore the important research question of how nmch of hill text understanding can be a.pproxinlai.ed by the finite-state approach. The following observations are very suggestive in this regard. We beliew~ that the most promising approach for full text understanding is the \"htterprel, ation as Abducl,iou\" approach elaborated in Hobbs el, al. (1993). There are i,hree basic operations in this approach, a.nd each of l.henl can be approximated in FASTUS technology. First, the syntactic structure is recognized and a Iogica.I form is produced. The corresponding operation in FASTUS is the recognition of phrases, that part of syntax that can be done reliably. Second, the logical form is proven al)ductively by back-chaining on axioms of the form", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "SUMMARY", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Tiffs can be approximated by adding flirt.her i)a.l, terns:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "(ga, b)Y(a,b) D X(a,b)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In addition to having a pattern for A X'ed B", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "(ga, b)Y(a,b) D X(a,b)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "we would also have a pattern for A Y'ed B", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "(ga, b)Y(a,b) D X(a,b)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Third, redundancies are spotted and merged to solve the coreference problem. As pointed out above, this is approximated in FASTUS by the operation of merging incidents. However, it nlust be realized that nnlch of the success of the FASTUS approach is in the clever ways it ignores much of the irrelevant information in the texl.. As we deal with texts in which more and more o[\" l.he information is relevant, this a.(Ivantage could well I)e Iosi, and a. gmmine, full text-understan(ling system will b(\" required.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "(ga, b)Y(a,b) D X(a,b)", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
| "BIBREF0": { | |
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| "title": "I':\\STIJS: A Syslenl for I~xtracting I nf(n'mation fi'om Nalm ral-l~a,guage Text\", SRI '!Pechnical Note 519, SRI International", | |
| "authors": [ | |
| { | |
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| "num": null, | |
| "urls": [], | |
| "raw_text": "I. llobbs, .]erry R., Douglas E. Appelt..loll, Bear, I)avid Israel, and Mabry Tyson, 1992. \"I':\\STIJS: A Syslenl for I~xtracting I nf(n'mation fi'om Nalm ral-l~a,guage Text\", SRI '!Pechnical Note 519, SRI International. Menlo Park, Ca|ifornia, November 1992.", | |
| "links": null | |
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| "raw_text": "Sundheim, Beth, ed., 1992. Proceeding.x, Fourth M('ssa.g(' Understanding Conference (MUC-4), Mcl,ean, Virginia, June 1992. Distributed by Morgan l(aufmann Pul)lish- ers, Inc., San Mateo, California.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF0": { | |
| "uris": null, | |
| "text": "Merging Incidents: Incident structures from different parts of the text are merged if they provide information about the same incident. *This research was supported in part by the Defense Advanced I~esearch Projects Agency under Contract ONI~I N00014-90-C,-0220 wi|.h the Office of Naval Research, in part by NTT Data, and in part by an SIII internal research and development grant. The views and c~mclusions ,'ontail~ed in this document are those of I.he ant|mrs and should not be interpreted as necessarily representing the ,dfi,:ial policies, either expressed or in,plied, of |he Defense Advanced l:{eseav,:h I:)roject:s Agency of the U.S. (;,~vernment.", | |
| "type_str": "figure", | |
| "num": null | |
| }, | |
| "TABREF2": { | |
| "html": null, | |
| "content": "<table><tr><td>THE PERFORMANCE</td><td>OF FASTUS</td></tr><tr><td colspan=\"2\">On the MUC-4 evahlation in June 1992, FASTUS was</td></tr><tr><td colspan=\"2\">among to top few systems, even tllough it had only been</td></tr><tr><td colspan=\"2\">tinder (levelopnient for five nlonths. On the TST3 set of</td></tr><tr><td/><td>There are patterns to accom-</td></tr><tr><td/><td>plish this. Two of them are as follows:</td></tr><tr><td/><td>Subject {Preposition NounGroup}*</td></tr><tr><td/><td>VerbGroup</td></tr><tr><td/><td>Subject Relpro {NounGroup I Oi, her}*</td></tr><tr><td/><td>VerbGroup {NounGroup [Other}*</td></tr><tr><td/><td>VerbGroup</td></tr><tr><td/><td>Tlle first of these patterns reads over prepositional</td></tr><tr><td/><td>phrases. Subject Relpro {NounGroup { Other}*</td></tr><tr><td/><td>VerbGroup</td></tr><tr><td/><td>Since tile finitie-state mechanisnl is nondeternlinistic, tile</td></tr></table>", | |
| "num": null, | |
| "text": "The second over relative clauses. The verb group at the end of these patterns takes the subject noun group as its subject. There is another pattern for capturing the COl)tent encoded in relative clauses:The n3a.yor, who was kidiral)ped yesterday, was foulid dead today.One branch discovers i.he iricident encoded in the rela.tive clause. Another branch marks t, ime through the relative clause arid then discovers the incident in the niain clause. \"Flies(, incidents are then merged. A similar device is used for\" conjoined verb phrases. The pattern Subject VelbGroup {Nourr(~4roup I Other}* Conju uction Verb(Iroup allows i,lie n]achine to nondeternlinistically skip over the first, conjunct and associate the subject with the verb group in the second colrjunct. This is llow, in the ahove examph', we were able to recognize Cristia.ni a~s the one who was accusing the FMLN of the crime.", | |
| "type_str": "table" | |
| } | |
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| } |