{ "paper_id": "E91-1004", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T10:38:02.176180Z" }, "title": "7 earl: A Probabilistic", "authors": [ { "first": "David", "middle": [ "M" ], "last": "Magerman", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Mitchell", "middle": [ "P" ], "last": "Marcus", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "This i)al)er describes a Ilatural language i)arsing algorith,n for unrestricted text which uses a prol)al)ility-I~ased scoring function to select the \"l)est\" i)arse of a sclfl,ence. The parser, T~earl, is a time-asynchronous I)ottom-ul) chart parser with Earley-tyl)e tol)-down prediction which l)ursues the highest-scoring theory iu the chart, where the score of a theory represents tim extent to which the context of the sentence predicts that interpretation. This parser dilrers front previous attemi)ts at stochastic parsers in that it uses a richer form of conditional prol)alfilities I)ased on context to l)rediet likelihood. T>carl also provides a framework for i,lcorporating the results of previous work in i)art-of-spe(;ch assignrlmn|., unknown word toocarl also provides a framework for i,lcorporating the results of previous work in i)art-of-spe(;ch assignrlmn|., unknown word tooa.rser can use granmlaticai information in word selection without slowing the speech recognition pro(~ess. Because of Pearl's interleaved architecture, one could easily incorporate scoring information from a speech rccogniz, cr into the set of scoring functions used in tile parser. Pearl could also provide feedback to the specch recognizer about the grammaticality of fragnmitt hypotheses to guide the recognizer's search.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "T'-earl's Capabilities", "sec_num": null }, { "text": "Partial Parses The main advantage of chart-based parsiug over other parsing algorithms is that the parser can also recognize well-formed substrings within the sentence in the course of pursuing a complete parse. Pearl takes fidl advantage of this characteristic. Once Pearl is given the input sentence, it awaits instructions a.s to what type of parse should be attempted for this i,lput. A standard parser automatically attempts to produce a sentence (S) spanning tile entire input string. llowever, if this fails, the semantic interpreter might be able to (Icriw-' some mealfiug from the sentence if given aon-ow'.rhq~pirig noun, w~.rb, and prepositional phrases. If a s,,nte,,ce f~tils I,o parse,, requests h)r p;trLial parses of the input string call be made by specifying a range which the parse l.ree should cover and the category (NP, VI', etc.).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "T'-earl's Capabilities", "sec_num": null }, { "text": "Tile al)ilil.y I.o llrodil('c i)artial parses allows the system i.o haildle ,nult.iple sentence inl~ul.s. In both speech alld I.~'x|. proc~ssing, il. is difficult to know where the (qld Of ;I S('llI,CIICe is. For illsta.llCe~ ouc CaUllOt reliably d,'l.eriiiitw wholl ;t slmakcr t(~.rlnillat\u00a2.s a selll,c,.ace ia free speech. Aml in text processing, abbreviations and quoted expressions produce anlbiguity abotll, sent,,.nc,, teriilinatioil. Wh,~ll this aildfiguil,y exists, .p,'a,'l can I),, qucri~'d for partial p;i.rse I.rccs for the given inpill., wh(,re l.ll(~ goal category is a sen(elite. Tin,s, if I.hc word sl.rittg ix a cl.ually two COmldcl.c S~'ld.elwcs, I.Im pars~,r call r,'l.urn I.his itd'orm;d.ioll. Ilow~,w,r, if I.hc word sl, r-itJg is oilly ()tic SCIItI~.IlCC, tllell it colilld~,l,c parse l.i't',, is retul'ned at lit.tie extra cost.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "T'-earl's Capabilities", "sec_num": null }, { "text": "Trai,mllility ()l.' of I.he lim;ior adva,d,agcs of the I~rohabilistic pars,,i's ix ti'ainalfility. The c(mditic, tm.I probabilities used by T'earl are estimated by using frequem:ies froth a large corpus of parsed sellte|lce~, rlahe pars~,d seill.enccs Ira,st be parsed ttSillg I.he grallima.r Ibrmalism which the `pearl will use.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "T'-earl's Capabilities", "sec_num": null }, { "text": "Assuming l.he g,'ammar is not rccursive in an unconstrained way, the parser can be traim~'d in an unsupervised mode. This is accomplished by framing the pars~,r wil.hotlt the scoring functions, and geuerating lilall~\" parse trees for each sentence. Previous work 9 has dclllonstrated that the correct information froth these parse l.rc~s will I)~\" reinforced, while the i,lcorrect substructure will not. M ultiple passes of re-Lra.iniqg itsing frequency data. from the previous pass shouhl cause t,lw fro(lllency I.abh,s 1.o conw'.rge to a stable sta.te. This JLvI)ol.hcsis has not yet beell tesl.cd. ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "T'-earl's Capabilities", "sec_num": null }, { "text": "While we haw; ,rot yet done ~-xte,miw~' testing of all of the Cal)abilities of \"/)carl, we perforumd some simple tests to determine if its I~erformance is at least consistent with the premises ,port which it is based. The I.cst s,'ntcnces used for this evaluation are not fi'om the \u00b0This is a.u Unl~,,blishcd result, reportedly due to Fujisaki a.t IBM .]apitll.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preliminary Evaluation", "sec_num": null }, { "text": "l0 In fact, h~r certain grail|liiars, th(.' fr(.~qllClicy I.~tl)les may not conw:rge at all, or they may converge to zero, with the g,','tmmar gc,tcrati,lg no pa.rscs for the entire corpus. This is a worst-case sccl,ario whicl, we do oct a,lticipate halq~cning.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preliminary Evaluation", "sec_num": null }, { "text": "training data on which the parser was trained. Using .p,'arl's cont(.'xt-free gr;unmar, i,h~.~e test sentences produced an average of 64 parses per sentence, with some sentences producing over 100 parses.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preliminary Evaluation", "sec_num": null }, { "text": "Word Part-of-speech Assignment To determine how \"Pearl hamlles unknown words, we remow'd live words from the lexicon, i, kuow, lee, de While this accuracy is expected for unknown words in isolation, based oil the accuracy of the part-ofspeech tagging model, the performance is expected to degrade for sequences of unk,lown words.", "cite_spans": [ { "start": 132, "end": 134, "text": "de", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Unknown", "sec_num": null }, { "text": "Acc0rately determining prepositional phrase attachnlent in general is a difficult and well-documented problem, llowever, based on experience with several different donmins, we have found prel)ositional phrase attachment to be a domain-specific pheuomenon for which training ca,t I)e very helpfld. For insta,tce, in the dirccl.ion-li,ldi,,g do,lmin, from aml to prepositional phrases generally attach to the preceding verb and not to any noun phrase. This tende,icy is captured iu the training process for .pearl and is used to guide the parscr to the more likely attach,nent with respect to ~he domain. This does not mean that Pearl will gel. the correct parse when the less likely attachme]tt is correct; in fact, .pearl will invariably get this case wrong, llowever, based on the premise that this is the less likely attachment, this will produce more correct analyses than incorrect. And, using a more sophisticated statistical model, this pcrfornla,lcc can easily be improved.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Prepositional Phrase Attachment", "sec_num": null }, { "text": "\"Pearl's performance on prepositional phrase attachmeat was very high (54/55 or 98.2% correct). The reaso,i the accuracy rate was so high is that/.lie directionfinding domain is very consistent in it's use of individt,al prepositions. The accuracy rate is not expected to be as high in other domains, although it certainly should be higher than 50% and we would expect it to bc greater than 75 %, although wc have nol. performed any rigorous tests on other (Ionmius to verify this. arsc for existing l)arsers, but ]hOSt had some granunatical atl ll)igllil,y which wouhl pro(lllce lllilitil)le i)arses. Ill fact, on 2 of tile 3 sciitences which were iucorrectly i)arsed, \"POal'l i)roduced the corl't~ct i);ll'SC ;is well, but the correct i)a,'se did not have the highest score.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Prepositional Phrase Attachment", "sec_num": null }, { "text": "The \"Pearl parser takes advantage of donmin-depen(lent information to select the most approi)riate interpretation of an inpul,. Ilowew'.r, i,he statistical measure used to disalnbiguate these interpretations is sensitive to certain attributes of the grammatical formalism used, as well as to the part-of-si)eech categories used to la-I)el lexical entries. All of the exl)erimcnts performed on T'carl titus fa,\" have been using one gra.linrla.r, one pa.rl.of-speech tag set, and one donlaiu (hecause of availability constra.ints). Future experime.nl,s are I)lanned to evalua.l,e \"Pearl's i)erforma.nce on dii[cre.nt domaius, as well as on a general corpus of English, arid ott dig fi~rent grammars, including a granunar derived fi'om a nlanually parsed corl)us.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Future Work", "sec_num": null }, { "text": "The probal)ilistic parser which we have described provides a I)latform for exploiting the useful information made available by statistical models in a manner which is consistent with existing grammar formalisms and parser desigus. 7)carl can bc trained to use any context-free granurlar, ;iccompanied I)y tile al)l)ropriate training matc,'ial. Anti, the parsing algorithm is very similar to a standard bottom-t,I) algorithm, with the exception of using theory scores to order the search.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": null }, { "text": "More thorough testing is necessary to inclosure 7)carl's performance in tcrms of i)arsing accuracy, partof-sl)eech assignnmnt, unknown word categorization, kliom processing cal)al)ilil.ies, aml even word selection in speech processing. With the exception of word selection, preliminary tesl.s show /)earl performs these ttLsks with a high degree of accuracy.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": null } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "lbwards Understanding Text with a Very Large Vocabulary", "authors": [ { "first": "D", "middle": [], "last": "Ayuso", "suffix": "" }, { "first": "", "middle": [], "last": "Bobrow", "suffix": "" }, { "first": "", "middle": [], "last": "It", "suffix": "" } ], "year": 1990, "venue": "Proceedings of the June 1990 DARPA Speech and Natural Language Workshop. llidden Valley", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Ayuso, D., Bobrow, It, el. al. 1990. 'lbwards Un- derstanding Text with a Very Large Vocabulary. 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Special thanks to Carl Weir and Lynette llirschman at Unisys for their valued input, guidance and support." }, "FIGREF1": { "type_str": "figure", "uris": null, "num": null, "text": "As an example of how the conditional-probability-b<~sed scoring flinction handles anlbiguity, consider the sentence Fruit, flies like a banana. i,i the dontain of insect studies. Lexical probabilities should indicate that the word \"flies\" is niore likely to be a plural noun than an active verb. This information is incorporated in the trigram scores, llowever, when the interliretation S --+ . NP VP is proposed, two possible NPs will be parsed, NP ~ nolnl (fruit) all d NP -+ noun nouu (fruit flies)." }, "FIGREF2": { "type_str": "figure", "uris": null, "num": null, "text": "implemented in Pearl provides uiany advantages over the tradil,ionai pilieline ar('hil,~+.(:l.ln'e, liut it, also iiil.rodu(-~,s c,:rl,a.ili risks. I)('+-('iSiOllS abollt word alld liarl,-of-sl)ee('h alnliiguity ca.ii I)e dolaye(I until synl,acl, ic I)rocessiug can disanlbiguate l,h~;ni. And, using I,he al)llroprial,e score conibhia.tion flilicl,iolis, the scoring of aliihigliOllS ('hoi(:es Call direct I, li~ parser towards I, he most likely inl,erl)re.tal, ioii ellicicutly. I lowevcr, with these delayed decisions COllieS a vasl,ly ~Jlllal'g~'+lI sl'arch spa(:('. 'l']le elf<;ctivelio.ss (if the i)arsi'.r dellen(Is on a, nla:ioril,y of tile theories having very low scores I)ased ou either uulikely syntactic strllCtllres or low scoring hlput (SilCii as low scores from a speech recognizer or low lexical I)robabilil,y). hi exl:)eriulenl,s we have i)erforn}ed, tliis ]las been the case. The Parsing Algorithm T'earl is a time-asynchronous I)ottom-up chart parser with Earley-tyi)e top-down i)rediction. The significant difference I)etween Pearl and non-I)robabilistic bol,tOllHI I) i)arsers is tha.t instead of COml)letely generating all grammatical interpretations of a word striug, Tcarl pursues i.he N highest-scoring incoml)lete theories ill the chart al. each I);mS. Ilowcw~r, Pearl I)a.,'scs wilhoul pruniny. All, hough it is ollly mlVallcing the N hil~hest-scorhig ] iiieOlill)h~l.~\" I, Jieories, it reta.his the lower SCOl'illg tlleorics ill its agl~ll(la. If I, he higher scorhlg th(,ories do not g(~lleral,e vial)It all,crnal.iw~s, the lower SCOl'illg l, lteori~'s IIHly I)(~ IISOd Oil SIliiSC~tllmllt i)a.'~scs." }, "FIGREF3": { "type_str": "figure", "uris": null, "num": null, "text": "TM An alternal.iw~ 1.o completely unsupervised training is I.o I.akc a parsed corpus for any domain of the same ];lllgil;Igl' IlSilig l,h,~ Salli,~ gra.iilllia.r, allllopipil is (lelincd to lie li(|lillll/'2) 7 )( PlizP1 )7)( Ill ) 'where x is lilly lillrt -of-speech.", "text": "The liilltilal iliforlll;ll.iOll el r ~ part-of-sl)eech trigram, See [4] for tintiler exlila.n,%l, ioli. GTlie trigrani .~coring funcl.ion actually ilsed by tile parser is SOill(:wh;il, tiler(: (:onllili(:al,t~d I, Ilall this.", "type_str": "table" } } } }