{ "paper_id": "P85-1002", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T09:39:38.932652Z" }, "title": "TEMPORAL I]~'RRI~C~S IN HEDICAL TEXTS", "authors": [ { "first": "Klaus", "middle": [ "K" ], "last": "Obermeier", "suffix": "", "affiliation": { "laboratory": "", "institution": "BatteIle's Columbus Laboratories", "location": { "addrLine": "505 K~ng Avenue CoLumbus", "postCode": "43201-2693", "region": "Oh\u00a3o", "country": "USA" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "P85-1002", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "The objectives of this paper are twofold, whereby the computer program is meant to be a particular implementation of a general natural Language", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "[NL] proeessin~ system [NI,PSI which could be used for different domains.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The first obiective is to provide a theory for processing temporal information contained in a well-structured, technical text. The second obiective is to argue for a knowledge-based approach to NLP in which the parsing procedure is driven bv extra Linguistic knowledRe. for analyzing domain-specific as well as temporal information in a well-defined text type.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The analysis, i.e. output, of the NLPS is a data structure which serves as the input to an expert system. The ultimate Real is to allow the user of the expert system to enter data into the system by means of NL text which follows the linguistic conventions of English. (2) map the parsed Linguistic structure into an event-representation;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "(3) draw temporal and factual inferences within the domain of Liver diseases;", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "(4) create and update a database containing the pertinent information about a patient.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "A SampLe Text:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "The user of my NLPS can enter a text of the format given in FiRure L L", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "The texts which the NLPS accepts are descriptive for a particular domain.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "The information-processing task consists of the analysis of Linguistic information into datastructures which are chronologically ordered by the NLPS.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "L This 80-year-old Cau=aslan female complained of nau.s~, vomlclnL abciommal swelhnl~ and jaundice.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "~. She h~[ dlal~ melhtus, credlL~'l wllh iosuiln for slx years ~fora aclm,~on.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "3. She ~ad ~lacl fll-~efmes~ p.sl~romcmuna[ complamu for many ye..lrs ancl occaalonai em~me.s of nau.s~ ancl vomum$ chr~ years ~'evlousiy -~ Four w~ics ~forc aclmlsslon snc dcveloo~l ptm across the u~\" aO~lomen.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "radmunll to the rlanlcs. The first module of the program analyzes each word by accessing a [exical component which assigns syntactic, semantic, and conceptual features to it. The second module consists of a bottom-up parser which matches the output from the lexical component to a set of augmented phrase structure rules 2. The third module consists of a knowledge base which contains the domain-specific information as well as temporal knowledge. The knowledge base is accessed during the processing of the text in conjunction with the augmented phrase structure rules. The output of the program includes a lexical feature assignment as given in Figure 2 , a phrase-structure representation as given in Figure 3 , and a knowledge representation as provided in Figure 4 .", "cite_spans": [], "ref_spans": [ { "start": 25, "end": 626, "text": "The first module of the program analyzes each word by accessing a [exical component which assigns syntactic, semantic, and conceptual features to it. The second module consists of a bottom-up parser which matches the output from the lexical component to a set of augmented phrase structure rules 2. The third module consists of a knowledge base which contains the domain-specific information as well as temporal knowledge. The knowledge base is accessed during the processing of the text in conjunction with the augmented phrase structure rules.", "ref_id": null }, { "start": 703, "end": 712, "text": "Figure 2", "ref_id": null }, { "start": 761, "end": 770, "text": "Figure 3", "ref_id": null }, { "start": 819, "end": 827, "text": "Figure 4", "ref_id": null } ], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "The resulting knowledge representation of mv NLPS consists of a series of events which are extracted from the text and chronologically ordered by the NLPS based on the stored knowledge the system has about the domain and ~enera [ temporal re[at ions. The final knowledge representation (see Figure 5 ) which my NLPS ~enerates is the input to the expert system or its database specialist. The final output o[ the expert system is a diagnosis of the patient. If a doctor were given a patient's case history (see Figure l) , he would read the text and try to extract the salient pieces of information which are necessary for his diagnosis. In this particular text type, he would be interested in the sign, symptoms, and laboratory data, as well as the medical history of the patient.", "cite_spans": [], "ref_spans": [ { "start": 291, "end": 300, "text": "Figure 5", "ref_id": "FIGREF2" }, { "start": 511, "end": 520, "text": "Figure l)", "ref_id": null } ], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "rlqlS 01[T~ I[IGI4TV-V[AIZ-0m0 ~O~ AG(, C~JC~SIa~ ~ RACE, . F~[NA~( N SEX' , ;~I[T -N([[D-NI[W , ~TE ~.ONPLA|N l ~UOT( ~.LASSI F \u2022 QUOT[ 5VAL, UI[ , , ' ,(D,, , OF me~p, ,N&US[A N SI~YM~TOM, VOMZT ki V S~[~iSyIIIIPTOM ~NGI, \u2022 ~. 60UNOadlV , ,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "I[VI[NT 1 SyIOT~l \u2022 k~kiS Jr A/V(:M | T / AS0~U[ NWIV~t SV([ L L. ]r NQ, d~4jNO|", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "The crucial point hereby is the temporal information associated with the occurrences of these data.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "In general, he would try to cluster certain abnormal manifestations to form hypotheses which would result in a coherent diagnosis.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "The clustering would be based on the temporal succession of the information in the text.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "Each manifestation of abnormalities [ will refer to as an \"event\". Pain can be characterized by its location, intensity. and nature (e.g., \"shooting\").", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "l", "sec_num": "2." }, { "text": "abnormalities found by the physician such as fever, jaundice, or swelling.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Signs:", "sec_num": null }, { "text": "notion of \"key event\" is further discussed in 4.3 \"Key Events\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4The", "sec_num": null }, { "text": "\"fever\" is a sign or a symptom is indicated by the verb.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Whether", "sec_num": null }, { "text": "Therefore, the verbs have features which indicate if the following is a sign or a symptom.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Whether", "sec_num": null }, { "text": "There are no explicit temporal indicators in (1), except the tense marker on the verb.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Whether", "sec_num": null }, { "text": "The doctor, however, knows chat case histories ordinarily use \"admission\" as a reference point. (2) She had diabetes mellitus, treated with insulin for six veers before admission.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Whether", "sec_num": null }, { "text": "The sentence in (2) requires a temporal concept \"year\" in conjunction with the numerical value \"six\", it also requires the concept \"duration\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Whether", "sec_num": null }, { "text": "to represent the meaning of for. The \"key event\" at admission is mentioned explicitly and must be recognized as a concept by the system. month, year, day, week, duration, period, i.e., \"for how long\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Whether", "sec_num": null }, { "text": "In addition to domain-specific knowledge, a person reading a text also uses his linguistic knowledge of the English grammar.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Flow of Control", "sec_num": "3.2" }, { "text": "The problem for a NLPS is how to integrate linguistic and extra linguistic knowledge. [t selects the constituents of a sentence, and groups them into larger \"chunks\", called phrases.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Flow of Control", "sec_num": "3.2" }, { "text": "The phrase types noun phrases [NP] and verb phrase [VPI contain procedures to form concepts (e.g., \"abdominal pain\"). These concepts are combined by function specialists. The schema identifies the current topic of the sentence, vlz., \"symptom\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Flow of Control", "sec_num": "3.2" }, { "text": "next encounters the word shootin~. This word has no further specification besides that of bein~ used as an adjective.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CROK", "sec_num": null }, { "text": "The head noun pain points to a more complex entity \"pain\" which expects further specifications (e.~., location, type). It first tries to find any further specifications within the :malvzed part of the NP.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CROK", "sec_num": null }, { "text": "[t finds shootin~ and adds this characteristic to the entity \"pain\".", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CROK", "sec_num": null }, { "text": "Since \"pain\" is usually specified in terms of its location, a place adverbial is expected. Upon the eqtry of across, the entity \"pain\" includes \"acro~s\" as a local ion marker, expect in~ as the next word a body-part. The next word, flank is a body-part, and the \"pain\" entity is completed. Note here, that the attachment of the preposition was ~uided by the information contained in the knowledge base.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CROK", "sec_num": null }, { "text": "The next word for is a function word which can indicate duration. To determine which adverbial for Lntroduces, the system has to wait for the information from the following Nl'-specialist.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CROK", "sec_num": null }, { "text": "After the numeric value \"three\", the temporal indicator \"dav\" identifies for as a duration marker. As soon as GROK verifies that a temporal indicator started an event, it fills in the information from the \"report-:from domain-specific Hirschman'ssystem and otherinthat extraGROK uses linguistic\u2022 organization:areeventsor-informationforanalyzingthetext,whereasganizedonatimeline,byHirschman relies primarily on available syntactickeyevents,calendardates,information.Therefore,Hirschman'ssystembefore/after chains?aspresentedin[Hirschman81]canneitherhandle anaphoric references to continuous states\u2022problems:how isimprecision,nor represent imprecision in time specification.fuzziness,andincompletenessof data handled?\u2022testing:howcanthesystemGROKisahighlyexploratoryprogram.be tested;by queries,proofs,Thelimitationsof the currentimplementationetc.?Does it have a consistencyare in three areas:checker?\u2022TheparseritselfdoesnotIn GROK,[ use an interval-based approachprovidethecapabilityofato temporalinformationprocessing.An eventchartparsersinceitwillis definedasan entityoffinite duration.notgivedifferentAsinIKamp79,3771,eventstructuresareinterpretations of a structurallytransformed into instants by the Russell-Wienerambiguoussentences.Thisconstruction.typeofstructuralambiguity,where one constituent can belong[nGROK,theNLPSprocessestemporaltotwoormoredifferent(nformat[onbyfirstassociatingaconceptconstructions,wouldnotbewithatemporalreference,thenevaluatingdetected.the extensionofthis event.The evaluationconsiderssyntactic(e.~.,adverbials)and\u2022Theknowledgebasedoesnotpragmaticinformation(currenttimefocus).have a fully implemented frameEacheventisrepresentedintheknowledgestructure.Each~enericframebase with information about when, for how long,has a certain number of slotsand what occurred.thatdefinetheconcept.Agenericconcept(e.g.,sign)The parser while analyzingthe sentences,musthaveslotswhichcontainorders these events according to a \"key event\".possibleattributesoftheThesingleeventscontaininformationaboutspecificframe(e.g.,wherethetemporalindicatorwhichis attachedtoisthesignfound;how severeadomain-soec~ficfact.Thesingleeventsisitsmanifestation).Theseareconnectedto therespective\"key event\".slotshavenotyetbeen\"Key events\" are domain-specific.[n general,implemented.Thenumberof[ qcipulatethatevervdomainhas alimitedframesisstrictlyi/mirednumber of such \"key events\" which provide thetothetemporalframesand\"hooks\"forthetemporalstructureofaa few exemplary ~enericframesdomain-speci fic text.necessary to process the text.CROK alsodiffersfromlogicaltheories\u2022 Therangeofphenomenais[nthatitdealswithdiscoursestructureslimited.Only \"before-admission\"and their conceptualrepresentations,not withreferencesarerecognizedby:solatedsentencesandtheirtruth value.[tthe system.Furthermore,slotsis differentfrom Kahn's rime specialist{Kahnthatpreventtheinheritance771 in that it uses domain knowledge and \"knows\"of events of limited durationsabout temporal relations of a particular domain.are not yet in place.Moreover, Kahn's program only accepts LiSP-likeinputandhandledonlyexplicittemporalin general,GROK is stillin a developmentalinformation.The use of domain-specific temporalstage at which a number of phenomena have vetknowledKealsoqet=; CROKapartfromAllen'sto be accounted for =hrough an implementation.l,\\[len 83] temporalinference engine approach.5.0CONCLUSIONGROKdiffersfromKamp'sdiscourse[n thispaper,[ argued for an integrationstructuresinthatitusesthenotionofofinsi%hcsRainedfromlinguistic,referenceintervalsthatarebasedonpsychological, and Al-based researchto provideconventiGnaltemporalunits(e.g.,day,week,\u2022 a pragmatic theory and cognitive mode[ of how temporaiiry: how is an eventmonth,year)to organizesingleeventsintodefined temporal inferences inthe cansystem; be explained ho~withinchronological order.theis temporal framework ofinformation treated computational informationvis-a-. !.; processing. A=he pragmatic whole theory system? focusesonGROK is in many respectssimilarto researchWhat the information search from the context algorithmsor (e.g., in-co-text,reportedin[Hirschman[98l]:bothsystemsference discourse situation, intentions of interlocutors) procedures are pro-dealwithtemporalrelationsinthemedicalvided? to explain linguistic behavior.domain;bothsyatemsdealwithimplicitandexplicittemporalinformation.GROK differs", "html": null, "text": "", "type_str": "table", "num": null } } } }