| { | |
| "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 too<lois, and other probal)ilistic models of lingvistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline a,'chitecture, lu preliminary tests, \"Pearl has I)ee.,i st,ccessl'ul at resolving l)art-of-speech and word (in sl)eech processing) ambiguity, d:etermining categories for unknown words, and selecting correct parses first using a very loosely fitting cove,'ing grammar, l", | |
| "pdf_parse": { | |
| "paper_id": "E91-1004", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "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 too<lois, and other probal)ilistic models of lingvistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline a,'chitecture, lu preliminary tests, \"Pearl has I)ee.,i st,ccessl'ul at resolving l)art-of-speech and word (in sl)eech processing) ambiguity, d:etermining categories for unknown words, and selecting correct parses first using a very loosely fitting cove,'ing grammar, l", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "All natural language grammars are alnbiguous. Even tightly fitting natural language grammars are ambiguous in some ways. Loosely fitting grammars, which are necessary for handling the variability and complexity of unrestricted text and speech, are worse. Tim standard technique for dealing with this ambiguity, pruning \u00b0This work was p,~rtially supported by DARPA grant I'Fhe grammar used for our experiments is the string ~ra.mmar used in Unisys' PUNI)IT natura.I language iindt'rsl.a ndi n/4 sysl.tml. gra.nunars I)y hand, is painful, time-consuming, and usually arbitrary. The solution which many people have proposed is to use stochastic models to grain statistical grammars automatically from a large corpus.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Attempts in applying statistical techniques to natura, I iangt, age parsi,lg have exhibited varying degrees of success. These successful and unsuccessful attempts have suggested to us that:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": ". Stochastic techniques combined with traditional linguistic theories can (and indeed must) provide a so-lull|on to the natural language understanding problem.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "* In order for stochastic techniques to be effective, they must be applied with restraint (poor estimates of context arc worse than none [7] ).", | |
| "cite_spans": [ | |
| { | |
| "start": 137, | |
| "end": 140, | |
| "text": "[7]", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "-Interactive, interleaved architectvres are preferable to pipeline architectures in NLU systems, because they use more of the available information in the decision-nmkiug process.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Wc have constructed a stoch~tic parser,/)earl, which is based on these ideas. The development of the 7~earl parser is an effort to combine the statistical models developed recently into a single tool which incorporates all of these models into the decisiou-making component of a parser, While we have only attempted to incorporate a few simple statistical models into this parser, ~earl is structured in a way which allows any nt, mber of syntactic, semantic, and ~other knowledge sources to contribute to parsing decisions. The current implementation of \"Pearl uses ChurclFs part-of-speech assignment trigram model, a simple probabilistic unknown word model, and a conditional probability model for grammar rules based on part-of-speech trigrams and parent rules.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "By combining multiple knowledge sources and using a chart-parsing framework, 7~earl attempts to handle a number of difficult problems. 7%arl has the capability to parse word lattices, an ability which is useful in recognizing idioms in text processing, as well as in speech processing. The parser uses probabilistic training from a corpus to disambiguate between grammatically ac(-i:ptal)h', structures, such ;m determining i)repo -sitional l)hrase attachment and conjunction scope. Finally, ?earl maintains a well-formed substring I,able within its chart to allow for partial parse retrieval. Partial parses are usefid botll for error-message generation aud for pro(-cssitlg lulgrattUllal,i('al or illCOllll)h;I,e .'~;l|-I,(~llCes. ht i)reliluinary tests, ?earl has shown protnisillg resuits in ha,idling part-of-speech ~ussignnlent,, preposit, ional I)hrase ;d,l, achnlcnl., ait(I Ilnknowlt wor(I catego-riza6on. Trained on a corpus of 1100 sentences from the Voyager direction-linding system 2 and using the string gra,ulm~r from l,he I)UNDIT l,aug,,age IhM,.rsl.atJ(ling Sysl,cuh ?carl correcl, ly i)a.rse(I 35 out of/10 or 88% of scIitellces sele('tcd frolu Voyager sentcil(:~}.~ tier used in the traini,lg data. We will describe the details of this exl)crimelfl, lal,cr.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In this I)al)cr , wc will lirsl, explain our contribul, ion l,o the sl,ochastic ,nodels which are used in ?earl: a context-free granunar with context-sensitive condil, ional probal)ilities. Then, we will describe the parser's architecture and the parsing algorithtn, l\"ina.lly, we will give the results of some exi)erinlents we performed using ?earl which explore its capabilities.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Recent work involving conl,ext-free a,.I contextsensitive probal)ilistic gramnlars I)rovide little hope for the success of processing unrestricted text osing I)roba.bilistic teclmiques. Wo,'ks I)y C, Ititrao and Grishman[3} and by Sharmau, .Iclinek, aml Merce,' [12] exhil)il, accllracy I'atos Iowq;r than 50% using supervised traininy. Supervised trailfiug for probal)ilisl, ic C, FGs requires parsed corpora, which is very costly in time and man-power [2] . lil otn\" illw~sl, igatiolls, w,~ hav,~ Iliad(; two ol)s(~rval,iolm which al,tcinl)t to Cxl)laiit l.h(' lack-hlstt'r i)erfornmnce of statistical parsing tecluti(lUeS:", | |
| "cite_spans": [ | |
| { | |
| "start": 262, | |
| "end": 266, | |
| "text": "[12]", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 454, | |
| "end": 457, | |
| "text": "[2]", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using Statistics to Parse", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Sinq)l~: llrol)al)ilistic ( :l,'(;s i)rovidc ycncTnl infornmlion about how likely a constr0ct is going to appear anywhere in a sample of a language. This average likelihood is often a poor estimat;e of probability.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using Statistics to Parse", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Parsing algorithnls which accumulate I)rol)abilities of parse theories by simply multiplying the,n overpenalize infrequent constructs.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using Statistics to Parse", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "?earl avoids the first pitfall\" by t,sing a contextsensitive conditional probability CFG, where cot ttext of a theory is determi,ted by the theories which predicted it and the i)art-of-sl)eech sequences in the input s,ml,ence. To address the second issue, Pearl scores each theory by usi.g the geometric mean of Lhe contextl,al conditional probalfilities of all of I.he theories which have contributed to timt theory. This is e(lt, ivalent to using the sum of the logs of l.hese probal)ilities.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using Statistics to Parse", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "~Spcclnl thanks to Victor Zue at Mlq\" h)r the use of the Sl)(:c(:h da.t;r from MIT's Voyager sysl, Clll.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using Statistics to Parse", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In a very large parsed corpus of English text, one finds I, Imt, I,be most freq.ently occurring noun phrase structure in I, Iw text is a nomt plu'asc containing a determiner followed by a noun. Simple probabilistic CFGs dictate that, given this information, \"determiner noun\" should be the most likely interpretation of a IlOUn phrase. Now, consider only those noun phrases which occur as subjects of a senl,ence. In a given corpus, you nlighl, liml that pronouns occur just as fre(luently as \"lletermincr nou,,\"s in the subject I)ositiou. This type of information can easily be cai)tnred by conditional l)robalfilities.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Finally, tmsume that the sentence begins with a pronoun followed by a verb. In l.his case, it is quite clear that, while you can probably concoct a sentence which fit, s this description and does not have a pronoun for a subject, I,he first, theory which you should pursue is one which makes this hypothesis.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The context-sensitive conditional probabilities which ?earl uses take into account the irnmediate parent of a theory 3 and the part-of-speech trigram centered at the beginning of the theory.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "For example, consider the sentence:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "My first love was named ?earl. (no subliminal propaganda intended) A theory which tries to interpret \"love\" as a verb will be scored based ou the imrl,-of-speecll trigranl \"adjective verb verb\" and the parent theory, probably \"S --+ NP VP.\" A theory which interprets \"love\" as a noun will be scored based on the trigram \"adjective noun w~rl).\" AIl,llo.gll Io.xical prollabilities favor \"love\" as a verb, I, he comlitional i)robabilities will heavily favor \"love\" as a noun in tiffs context. 4", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "According to probability theory, the likelihood of two independent events occurring at the same time is the product of their individual probabilities. Previous statistical parsing techniques apply this definition to the cooceurrence of two theories in a parse, and claim that the likelihood of the two theories being correct is the product of the probabilities of the two theories. 3The parent of a theory is defined as a theory with a CF rule which co.tains the left-hand side of tile theory. For instance, if \"S ---, NP VP\" and \"NP --+ det n\" are two grammar rules, the first rule can be a parent of tile second, since tl,e left-hand side of tile second \"NP\" occurs in the right-hand side of the first rule.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "4In fact, tile part-of-speech tagging model which is Mso used in ~earl will heavily favor \"love\" as a noun. We ignore this behavior to demonstrate the benefits of the trigram co.ditioni.g. 'l?his application of probal)ility theory ignores two vital observations el)out the domain of statistical parsing:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Two CO,lstructs .occurring in the same sentence are ,lot n,:ccssa,'ily indel)cndc.nt (and frequ~ml.ly are not). If the indel)el/de//e,, ;msuniption is violated, then tile prodl,ct of individual probabilities has no meaning with ,'espect to the joint probability of two events. \u2022 Theory scores shouhl not deliend on thc icngth of the string which t, hc theory spans.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 ~l)al'S(~ data (zero-fr~:qllelicy eVl;lltS) ~llid evell zero-prolJahility ew;nts do occur, and shouhl not result in zero scoring Lheorics.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Theory scores should not discrinfinate against unlikely COlistriicts wJl,'.n the context liredicts theln.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The raw score of a theory, 0 is calculated by takiug I,he. i)rodul:l, of the \u00a2onditiona.I i)rol)ability of that theory's (',1\"(i ride giw;il the conl,ext (whel'l ~, COlitelt is it I)iirl,-of-sl)(~ech I,rigraln a.n(I a l)areiit I,heol'y's rule) alid I, he score of tim I, rigrani:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": ",5'C:r aw(0) = \"P(r {tics I(/'oPl 1'2 ), ruic parent ) sc(pol,! 1)2 )", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "llere, the score of a trigram is the product of the mutual infornlation of the part-of-speech trigram, 5 POPII~2, and tile lexical prol)ability of the word at the Ioeatioil of Pi lieing assigiled that liart-of-specch pi .s In the case of anlhiguil,y (part-of-speech ambiguity or inuitil)le parent theories), the inaxinuim value of this lirothict is used. The score of a partial theory or a conl-I)lete theory is the geometric liieali of the raw scores of all of the theories which are contained in that theory. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This scoring function, although heuristic in derivation, provides a nlethod Ibr evaluating the value of a theory, regardless of its length. When a rule is first predicted (Earleystyh;), its score is just its raw score, which relireseuts how uiuch {,lie context predicts it. llowever, when the parse process hypothesizes interpretations of tile senteuce which reinforce this theory, the geornetric nlean of all of the raw scorn of the rule's subtree is used, rcllrescnting the ow,rall likelihood or I.he i.heory given the coutcxt of the sentence.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Theory Length Independence", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Low-freqlteltcy Ew:nts AII.hol,gll sonic statistical natural language aplili('ations enllAoy backing-off e.stimatitm tcchni(lues[ll] [5] to handle low-freql,eney events, \"Pearl uses a very sintple estilnation technique, reluctantly attributed to Chl,rcl, [7] . This technique estiniatcs the probability of au event by adding 0.5 to every frequency count. ~ Low-scoring theories will be predicted by the Earley-style parscr. And, if no other hypothesis is suggested, these theories will be pt, rsued. If a high scoring theory advauces a theory with a very low raw score, the resulting theory's score will be the geonletric nlean of all of the raw scores of theories contained in that thcory, and thus will I)e nluch higher than the low-scoring theory's score. Sitlce this sentence is syntactically ambiguous, if the first hypothesis is tested first, the parser will interpret this sentence incorrectly.", | |
| "cite_spans": [ | |
| { | |
| "start": 133, | |
| "end": 136, | |
| "text": "[5]", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 255, | |
| "end": 258, | |
| "text": "[7]", | |
| "ref_id": "BIBREF6" | |
| } | |
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| "section": "Theory Length Independence", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "ll0wever, this will not happen in this donlain. Since \"fruit flies\" is a common idiom in insect studies, the score of its trigram, noun noun verb, will be much greater than the score of the trigram, noun verb verb. Titus, not only will the lexical probability of the word \"flies/verb\" be lower than that of \"flies/noun,\" but also tile raw score of \"NP --+ noun (fruit)\" will be lower than 7We are not deliberately avoiding using ,'ill probability estinlatioll techniques, o,,ly those backillg-off techaiques which use independence assunlptions that frequently provide misleading information when applied to natural liillgU age.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Example of Scoring Function", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "that of \"NP -+ nolln nolln (fruit flies),\" because of the differential between the trigram score~s.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Example of Scoring Function", | |
| "sec_num": null | |
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| { | |
| "text": "So, \"NP -+ noun noun\" will I)e used first to advance the \"S --+ NI ) VP\" rid0.. Further, even if the I)arser a(lva.llCeS I)ol,h NII hyliol,h(++ses, I,he \"S --+ NP . VI'\" rule IlSilig \"N I j ---+ liOllll iiOlln\" will have a higher s(:ore l, hau the \"S --+ INIP . Vl )'' rule using \"NP -+ notul.\" The liarsing alg(u'ithill begins with the inl)ut word lati,ice. An 11 x It cha.rl, is allocated, where It iS the hmgl, h of the Iongesl, word sl,rillg in l,lie lattice, l,\u00a2xical i'uh~s for I,he inliut word lal.l, ice a, re inserted into the cha.rt. Using Earley-tyl)e liredicLi6u, a st;ntence is pre-(licl.ed at, the beginuilig of tim SClitence, and all of the theories which are I)re(licl.c(I l)y l, hat initial sentence are inserted into the chart. These inconll)lete theetics are scored accordiug to the context-sensitive conditional probabilities and the trigram part-of-speech nlodel. The incollll)lel.e theories are tested in order by score, until N theories are adwl.nced, s The rcsult.iug advanced theories arc scored aud predicted for, and I, he new iuconll)lete predicted theories are scored and aWe believe thai, N depends on tile perl)lcxity of the gralillllar used, lint for the string grammar used for our CXl)criment.s we ,tsctl N=3. [\"or the purl)oses of training, a higher N shouhl I)(: tlS(:(I ill order to generaL(: //|ore I)a.rs(:s. added to the chart. This process continues until an coml)lete parse tree is determined, or until the parser decides, heuristically, that it should not continue. The heuristics we used for determining that no parse can I)e Ibun(I Ibr all inlmt are I)ased on tile highest scoring incomplete theory ill the chart, the number of passes the parser has made, an(I the size of the chart.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Example of Scoring Function", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Besides nsing statistical methods to guide tile parser l,hrough I,h,' I)arsing search space, Pearl also performs other functions which arc crucial to robustly processing UlU'estricted uatural language text aud speech.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "T'-earl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Handling Unknown Words Pearl uses a very simple I)robal)ilistic unknown word model to hypol.h(nsize categories for unknown words. When word which is unknown to the systenl's lexicon, tile word is assumed to I)e a.ny one of the open class categories. The lexical i)rol);d)ility givell a (-atcgory is the I)rol)ability of that category occurring in the training corpus.", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "T'-earl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Idiom Processing and Lat, tice Parsing Since the parsing search space can be simplified by recognizing idioms, Pearl allows tile input string to i,iclude idioms that span more than one word in tile sentence. This is accoml)lished by viewing the input sentence as a word la.ttice instead of a word string. Since idion}s tend to be uuand)igttous with respect to part-of-speech, they are generally favored over processing the individual words that make up the idiom, since the scores of rules containing the words will ten(I to be less thau 1, while a syntactically apl)rol)riate, unambiguous idiom will have a score of close to 1.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "T'-earl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The ahility to parse a scnl.epce wil, h multiple word hyl)otlmses and word I)oulidary hyl)othcses makes PeaH very usehd in the domain of spoken language processing. By delayiug decisions about word selection I)ut maintaining scoring information from a sl)eech recognizer, tlic I>a.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": [], | |
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| "text": "ONR contract No. N00014-89-C-0171 by DARPA and AFOSR jointly under grant No. AFOSR-90-0066, and by ARO grant No. DAAL 03-89-C(1031 PRI. Special thanks to Carl Weir and Lynette llirschman at Unisys for their valued input, guidance and support." | |
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| "uris": null, | |
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| "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, all<l liS~: I, he fl'~:-iIIIpllCy dal,a frolli I.hal, corpllS ;is I, hc iliil, ial I,ra.iliiilgj iilal, erial for I, he liew corpus. This allproach should s,)i'vt~ ()lily I,o iiiinilnize I, he lilliilber of UliSUllCrvised passes reqilired for l.lio freqileilcy dal, a I,o converge." | |
| }, | |
| "FIGREF4": { | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null, | |
| "text": "scribe, aml station, and tried to parse the 40 sample sentences I,sing the simple unknown word model previe,rely d,:scribcd. I,i this test, the pl'onollll, il W~L,'q assigncd the correct. i)art-of-speech 9 of 10 I.iiiies it occurred in the test ,s'~'nt~mces. The nouns, lee and slalion, were correctly I.~tggcd 4 of 5 I.inics. And the w;rbs, kltow and describe, were corl'~cl.ly I, aggcd :l of :l tiilles." | |
| }, | |
| "FIGREF5": { | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null, | |
| "text": "Performance on Unknown Words in Test Sen-I, ences" | |
| }, | |
| "FIGREF6": { | |
| "type_str": "figure", | |
| "uris": null, | |
| "num": null, | |
| "text": "ate 92 % 100 % 100 % 98.2 % I\"igure 2: Accl,racy Rate for Prepositional Phr;~se At-I.achnlcnt, I)y l)reposition Overall Parsing Accuracy The 40 test sentences were parsed by 7)earl and the highest scoring parse for each sentence was compared to the correct parse produced by I'UNI)rr. Of these 40 s~llt.encos, \"])~'.;I.l'I I),'odu('ed p;t.rsr: tl'(?t:s for :18 of ti,enl, alld :15 of I, he.sc i)a.rsc tree's wt~t'[~\" {:(liliv;i.I(:lll, I,o I,hc cor-I'~:Cl, I)al'Se i)roducetl by I)ulldil,, for an overall at;cura(:y M;itly of Lilt: I,(?st SelltellCCS W(?l't. ~ IIot (lillicult I,o i)" | |
| }, | |
| "TABREF1": { | |
| "html": null, | |
| "num": null, | |
| "content": "<table><tr><td>llopipil is (lelincd to lie li(|lillll/'2) 7 )( PlizP1 )7)( Ill ) '</td><td>where x is lilly lillrt -</td></tr><tr><td>of-speech.</td><td/></tr></table>", | |
| "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" | |
| } | |
| } | |
| } | |
| } |