| { | |
| "paper_id": "C96-1012", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T12:52:11.146627Z" | |
| }, | |
| "title": "To what extent does case contribute to verb sense disambiguation?", | |
| "authors": [ | |
| { | |
| "first": "Atsushi", | |
| "middle": [], | |
| "last": "Fuji1", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Inui", | |
| "middle": [], | |
| "last": "Kentaro", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tokunaga", | |
| "middle": [], | |
| "last": "Takenobu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tanaka", | |
| "middle": [], | |
| "last": "Hozmni", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Word sense disambugation has recently been utilized in corpus-based aI)proaches, reflecting the growth in the number of nmehine readable texts. One (:ategory ()f al)l)roa(:hes disambiguates an input verb sense based on the similarity t)etween its governing (:its(; fillers and those in given examl)les. In this palter , we introdu<:c the degree of (:<mtriblltion of cast; to verb sells(', disambignation intt) this existing method, in this, greater diversity of semanti(: range of case filler examples will lead to that ease contributing to verb sense disambiguation more. We also report th(; result of a coml)arative ext)eriment, in which the t)erfornlance of disaml)igui~tion is iml)rt)ved t)y considering this notion of semantic contribution.", | |
| "pdf_parse": { | |
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| "abstract": [ | |
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| "text": "Word sense disambugation has recently been utilized in corpus-based aI)proaches, reflecting the growth in the number of nmehine readable texts. One (:ategory ()f al)l)roa(:hes disambiguates an input verb sense based on the similarity t)etween its governing (:its(; fillers and those in given examl)les. In this palter , we introdu<:c the degree of (:<mtriblltion of cast; to verb sells(', disambignation intt) this existing method, in this, greater diversity of semanti(: range of case filler examples will lead to that ease contributing to verb sense disambiguation more. We also report th(; result of a coml)arative ext)eriment, in which the t)erfornlance of disaml)igui~tion is iml)rt)ved t)y considering this notion of semantic contribution.", | |
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| "section": "Abstract", | |
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| "text": "Word sense disambiguation is a crucial task in many kinds of natural language I)rot:essing at)l)lications, such as word selection in iIla(;hine translation (Sato, 1991) , pruning of syntactic structures in parsing (l,ytinen, 1986; Nagao, 11994) an(l text retrieval (Krovets and Croft, 1992; Voorht'.es, 1993) . Various researches on word sense disamil)ignation have recently been utilized in (:orlms-based apt)roache.s, reflecting the growth in the numlmr of machine readable texts. Unlike rule-basel1 ~l)l)roa('.hes, eortms-l)asext al)proa(:hes free us fl'om the task of generalizing observed 1)hent)Illena to l)roduce rnles for word sense, disaln-])igmttion, e.g. subt:ittegorization rules. Cortmsbased al)proaches are exet:ut(;(1 based on the intuitively t'easibh', assmnption that the higher the degree of similarity betwee, n the context of an illput word and tim context ill which tit(; word apl)cars in a sens(~' in a tort)us , the more plausible it becomes that the word is used in the same s(.~nse. Corpus-/)ased m(;thotls are. classified into two ap-1)rt)aches: examI)le-I)ased approaches (Kurohashi and Nagao, 1994; Urmnoto, 1994) and statisticbased apl)roa (:hes (l~rown et al., 1991 ; 1)tLglm and Itai, 1!)94; Niwa and Nitta, 11994; Schiitze, 1992; Ym'owsky, 1995) . We follow the examt)h>based apl)roach ill exl)laining its effe.etivity for verb sense disamibiguation in Japanese.", | |
| "cite_spans": [ | |
| { | |
| "start": 156, | |
| "end": 168, | |
| "text": "(Sato, 1991)", | |
| "ref_id": "BIBREF10" | |
| }, | |
| { | |
| "start": 214, | |
| "end": 230, | |
| "text": "(l,ytinen, 1986;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 231, | |
| "end": 244, | |
| "text": "Nagao, 11994)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 265, | |
| "end": 290, | |
| "text": "(Krovets and Croft, 1992;", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 291, | |
| "end": 308, | |
| "text": "Voorht'.es, 1993)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1099, | |
| "end": 1126, | |
| "text": "(Kurohashi and Nagao, 1994;", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 1127, | |
| "end": 1141, | |
| "text": "Urmnoto, 1994)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1169, | |
| "end": 1195, | |
| "text": "(:hes (l~rown et al., 1991", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1223, | |
| "end": 1245, | |
| "text": "Niwa and Nitta, 11994;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1246, | |
| "end": 1261, | |
| "text": "Schiitze, 1992;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1262, | |
| "end": 1277, | |
| "text": "Ym'owsky, 1995)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A representative example-based method for verb sense disambiguation was proposed by Kurohashi and Nagao (Kurohashi's inethod) (Kurohashi rand Nagao, 1994) . Their method uses an 0,xamph; database, containing examples of collocations as in figure 1. Figure 1 shows a fragment of tim entry associated wittl the Japan(;se verb to'ru. As with most words, the ve, rb to'r\"\u00a2t has multipie senses, examples of whit:h are \"to take/steal,\" \"to attain,\" \"to subst'ril)e\" and \"to reserve,\" The database gives one or more case frame(s) associated with tilt', verbs for each of their senses. In .Japanese, a coutI)lelnt;nt Of a verb, which is a constituent of the case frame of the verb, consists of a nonii phrase (case filler) followed by a case marker such ms ga (nominative) or o (accusative). The database has ~m example set of case fillers for each case. As shown in figure 1, examples of a comi)lement c.an be considered as an extensional description of the selectional restriction on it.", | |
| "cite_spans": [ | |
| { | |
| "start": 142, | |
| "end": 154, | |
| "text": "Nagao, 1994)", | |
| "ref_id": "BIBREF7" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 249, | |
| "end": 257, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| "text": "The task (:onside.red in this paper is %o interpret\" a verb in an input s('.ntcnt:e, i.e. to choose ()lit) sense from a set of candidate senses of the verb. Given an input sentence, Kurohashi's method interprets the verb in the input by computing semantic similarity between the input and exalnples. For this computation, Kurohashi's nmthod experimeIltally uses the Ja,panese word thesaurus Bunruigoihyo (National-Language R(> search Institute, 1964) . As with Inost thesauruses, the length of the 1lath between two words in Bunr'uigoihyo is exl)e, eted tt) reflect the similarity be,tween them. (1) hisho .qa sh, indaish, a o tor,u.", | |
| "cite_spans": [ | |
| { | |
| "start": 434, | |
| "end": 450, | |
| "text": "Institute, 1964)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "(set:retm'y-NOM) (siegel,trig (:ar-ACC)", | |
| "cite_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| "text": "lit this examph',, it may t)e judged according to tigure 2 that h, ish, o (\"secretary\") and shindaisha (\"sleeping car\")in (1)i~l(, ,'~emantically similar to joshu (\"assistant\") att(l hikbki (\"airplane\"), re-Sl)ectively, which are cxamI)les that collocate with t(rru (\"to reserve\"). As sut'h, the sense of rot'u, in (1) can be interpreted as \"to reserve.\" llowever, in Kurohashi's nmthod, several usefifl properties for verb disambuguatittn are missing:", | |
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| "text": "1. httuitively speaking, the, contribution of the 2. The seleetional restriction of a certain case is stronger than those of others. For example, in tile accusative, the selectional restriction of \"to subscribe\" is stronger than that of \"to take/steal\" which Mlows various kinds of objects as its case filler.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "EQUATION", | |
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| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "sur{ (pickpocket) } kanojo (she) ga an'i ( }n'ot her) /,:a ,,,", | |
| "eq_num": "(he)" | |
| } | |
| ], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In this p~tt)er, we improve on Kurohashi's method by introducing a formalization of these notions, and report the result of a comparative experiment.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Property 1 in section 1 is exemplified by the input sentence (2).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Motiw, tion", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "(2) (presideut-NOM) (magazine-ACe)", | |
| "cite_spans": [], | |
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| "section": "Motiw, tion", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "The nominative, shachd (\"company president\"), in (2) is found in the %o attaiIf' ease frame of torn and there is no other co-occurrence in any other sense of toru; therefore, the nominative supports an interpretation \"to attain.\" On the other hand, \u00a9 nominative accusative Figure 3 : The semantic ranges of the nominative and accusative with verb torn the accusative, ,sh, gtkanshi (\"magazine\"), is most similar to the examples included ill tile accusative of the \"to subscribe\" and therefore the accusative supports another interpretation \"to snt)scribe.\" Although tile most plausible interpretation here is actually the latter, Kurohashi's method would choose tile former since (a) the degree in which the nominative sut)ports \"to attain\" happe.ns to be stronger than the degree in which the accusatiw'~ supports \"to subscribe,\" and (b) their method always relies equally on the similarity in the nominative and the accusative. Itowever, in the case of torn, since the semantic range of nouns collocating with the verb in the nominative does not seem to have a strong delinearization in a semantic sense, it would be difficult, or even risky, to properly interpret the verb sense based on tile similarity in the nominative. In contrast, since the ranges are diverse in the accusative, it would lm fe.asible. to rely more strongly on the similarity in the accusative. This argument can be illustrated as in figure 3, in which the symbols \"1\" and \"2\" denote example case fillers of different case fraines respectively, and an input sentence includes two case fillers denoted by \"x\" and \"y.\" The figure shows the distribution of example case fillers tienoted by those symbols in a semantic space, where the semantic similarity between two case fillers is represented by the physical distance between two symbols. In the nominative, since \"x\" ha.ptmns to })e iliuch cl()s(;r to & \"2\" th~Ln ~tlly \"1~\" \"X\" IIh~y be estimated to belong to the range of \"2\"s all, hough \"x\" ae('.ually belongs to both sets of \"l\"s a.nd \"2\"s. Ill the accusative, however, \"y\" would he prol)erly estimated to belong to \"l\"s due. to (;tie mutuM indet)en(lence of the two ac(:usative case filler sets, even though examples (lid not fully (:over e~tch of the ranges of \"t\"s and \"2\"s. Note that this diiferen(:e would he critieM if example (1,~t~ w(;re sparse. This argument suggests that we introduce, the degree of (:ontribution of case to verb sense disaml)iguation. One may argue that this l)roperty ca.n tie generMized as the notion tha. (1,rother-NOM) (toy-ACC) ('?)", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 273, | |
| "end": 281, | |
| "text": "Figure 3", | |
| "ref_id": null | |
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| ], | |
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| "section": "Motiw, tion", | |
| "sec_num": "2" | |
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| "text": "In (3) th(! mosl: plausible inte.rpretati(m of l.or,u is \"to st(~al.\" Tim nonlina.tiv(~ does llot give mu(:h inf(~rtna.ti()n for interl)r(Mtip; the vert) for t;h(~ same reason as exa.uiph+ (2). lu the accusative, the datallase in tigure] has two example case lillers that arm (;(lU;fl]y similar to om, ocha (\"toy\"): saiftt (\"wallet\") and h, ikaki (\"airplane\"). These exami)les equMly SUl)t)ort two (lifferent interi)ret;ttions: \"t() steal\" mM \"to res(;rve,\" which me.ires thnt the verl) sense aml)igui(;y still rcmMns. ]lea'e, one ina.y noti(:e thai; since tile a(:(;ust~l;ive examples in tile C;tSe [l'i/,lIle of [,OT'lt (\"to reserve:') ~Ll'e, less diverse in niea.uing than the other case fr;tmes, the se[(!el;ion;tl restrit:l;ion on the ;t(:(:us~tiv(; of to'v'tt ('%o restarve') is relatively strong, ~md thus that it can be estiniated tt) lie reJatively ilnplausible for ornocha (\"toy\") to sa.tis[y it. If su(:h reasoning is correct, given that the ex~mll)les in the accusative of tor\"u (\"to steal\" ) are most widely distributed, the inlmt verl) (:an lie interl)reted as \"to steal.\" The consideration M)ove motivated us to introduce the notion of rela.tive strength t)f select]ohM restriction into our e~xaJnple-1)ased verb sense disalnbigu~tion method.", | |
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| "section": "Motiw, tion", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "We assume that inputs ~re simple sentences, e~mh one of which consists of a sequellce of eases fl)llowe.d by their governing verb. The. task is to identify the sense of each input verb. The set of verl) senses we use are those defined in the existing machine re~tdal)le (li(:ti()llary \"IPAL\" (IPA, 1987), which also (:olltains example case fillers as shown in figure .t. As well as Kuroh~tshi's method the similarity between two (:as(; tillers, or more pre-('isely the semantic-head nouns of them, is corn- 1: The relation I)t'.tweell the length of path I)e-|;ween two i[()llns A\" {Mid Y (lt:7/,(.k', }:)) ill IJtL:l~r,Lil:o'ibye and the similarity hetween them (.sirn(X, Y)) [~a.n(X,Y) l 0,. . : 2 9468 l012 t [ s.zm(A, ~ ) tl 10 8 7 5 0 tinted by using IIv, rwuigoih, yo (National-Language l{esearch lnstil; ute, 1964) . Following Kurohashi's method, we define .sim(X,~), whi(:h stands for the silnilarity 1)etween words X mM Y, as in tattle 1. It should he noted here that both nl(~t;h()ds ~tre theoreti(:ally indel)endent of wh;tt resources }ire use(t. ~lb illustl'~te tit(; overall a.lgorithm, we r(~t)la.(:(~ the illustra.tive cases mentioned in section 1 wilh a slightly re(ire gelmral case as in figure. 4. The iut)ut is {nc,-'mc), nc:'m.ce, v}, where he. i all!notes the case filler in the case ci, a.nd 'ntc~ denotes the case maker of <:i. The candidates of ilH;(~rl)ret;ttion for v, which ~re ,sl, ,s2 ~md s3, are deriv(;d froln the datal)ase. The. d;ttal)ase also gives a set ~;si c i of case filler ex~mq)les for each case. c:.i (if each sense si. \" \" den()tes thnt the eorresl)ondit~t~; case is not allowed. eategrize the. case ct i. lit ('ontrast, s~ will not be reject(;d ~tt this step. This is based on the fact that in ,J;tl)~UleSe , t'~ts(!s t:tm lie easily omitted if they ;~re inferable from the given context. Thereafter, the system comt)utes the 1)la.usibility of the remaining candidates of interpret~ttion and chooses the most pla,usit)le interpretatiou as its output, in Kurohashi's method, tim plausil>ility of tui interl)retation is eonq)uted t)y aver;tging; the degree of similarity between the inl)ut com-1)leinent and the exalnple complements 'e for each case &S in e([u&tiOll (1): where P(,q) is thc [)[~LU-I Since I I'AI, does not necessarily eliIlll~(~lligte all the possible optional cases, the ~LbSellCe of C;tse C I from \"v (.~a) in the figure may denotl; that \u00a2:1 is optioual. If so, the interpretation s:) sht)uld not be dis(:arded in this stooge. To avoid this problem, we use the same technique as used in Kurohashi's method. That is, we deline several particular ea.ses befl)reha.nd, such as l, he nomin~d;ive, the accusative i~Iltl the (l~ttive, to be. obligatory, and impose tilt; graulm~rti(:~tI t:ase fHtllle t:onstrmnt as ~d)ove only in those obligatory (:ases. ()ptionality of case needs to be further exl)h)red. sibility of interpreting the input verb as sense 3, and SIM (nc, $~,c) is the degree of the similarity between the input complement nc and example complements $s,c. ws is the weight on an interpretation 3 such that more obligatory cases imposed by s being found in tile input, will lead to a greater value of the weight a. P(3) = w3 E SIM (nc, Ss,c) (1) c SIM(nc, \u00a33,c) is the maximum degree of similarity between nc and each of \u00a33,e as in equation 2. SIM(, c, &,e) = max sim(,+c,", | |
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| "start": 662, | |
| "end": 675, | |
| "text": "(.sirn(X, Y))", | |
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| "start": 773, | |
| "end": 809, | |
| "text": "(National-Language l{esearch lnstil;", | |
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| { | |
| "start": 810, | |
| "end": 820, | |
| "text": "ute, 1964)", | |
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| { | |
| "start": 2921, | |
| "end": 2931, | |
| "text": "(nc, $~,c)", | |
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| "start": 3200, | |
| "end": 3210, | |
| "text": "(nc, Ss,c)", | |
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| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "ec~8,c", | |
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| "text": "In our method, on the other hand, for the reason indicated in section 1, we introduce two new factors:", | |
| "cite_spans": [], | |
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| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 contribution of case to verb sense disambignalion (CCD),", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 relative strength of selectional restriction (RSSR).", | |
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| "section": "Algorithm", | |
| "sec_num": null | |
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| { | |
| "text": "First, in regard to CCD, we compute the plausibility of an interpretation by the weighted average of the degree of similarity for each case as in equation (a), replacing equation 1.", | |
| "cite_spans": [], | |
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| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "P(3) = w3 Ec g3,e)\" CCD(c) Ec CUD(c) (3)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Here, CCD(c) is a newly introduced weight, such that CCD(c) is greater when the degree of case e's contribution is higher.", | |
| "cite_spans": [], | |
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| "sec_num": null | |
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| "text": "Second, in regard to RSSR, the stronger the selectional restriction on a case of a case frame is, the less plausible all input complement satisfies that restriction as mentioned in section 1. Note here that tile plausibility of an interpretation of an input verb can be regarded as the plausibility that the input complements satisfy the selectional restriction associated with that interpretation. This leads us to replace SIM (nc, Es,c) in equation 3with PSS(nc, \u00a3s,c) , which denotes the plausibility that the case filler nc satisfies the selectional restriction described by the example case fillers ~S,C.", | |
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| { | |
| "start": 428, | |
| "end": 438, | |
| "text": "(nc, Es,c)", | |
| "ref_id": null | |
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| { | |
| "start": 457, | |
| "end": 470, | |
| "text": "PSS(nc, \u00a3s,c)", | |
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| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
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| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "P(3) = w3 Ec PSS('nc, g3,c) \u2022 CCD(c) EcCCD(c)", | |
| "eq_num": "(4)" | |
| } | |
| ], | |
| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "From the assumption that PSS (nc,Es,c) should be greater for a larger SIM (ne,\u00a3s,c) and lesser relative strength of the selectional restriction described by \u00a3s,c, we can derive equation 5.", | |
| "cite_spans": [ | |
| { | |
| "start": 29, | |
| "end": 38, | |
| "text": "(nc,Es,c)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 74, | |
| "end": 83, | |
| "text": "(ne,\u00a3s,c)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Here, RSSR(3, c) denotes the relative strength of tile selectional restriction on a case c associated with a sense 3.", | |
| "cite_spans": [ | |
| { | |
| "start": 6, | |
| "end": 13, | |
| "text": "RSSR(3,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 14, | |
| "end": 16, | |
| "text": "c)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "PSS(nc, \u00a3s,c) = SIM(nc, Ss,c) -RSSR(3, c)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "3For more detail, see Kurohashi's paper (Kurohashi and Nagao, 1994) .", | |
| "cite_spans": [ | |
| { | |
| "start": 22, | |
| "end": 67, | |
| "text": "Kurohashi's paper (Kurohashi and Nagao, 1994)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "PSS(nc, \u00a3s,c) = SIM(nc, Ss,c) -RSSR(3, c)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Computation of CCD and RSSR", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The degree of contribution of case to verb sense disambiguation (CCD) is computed in the following way. The degree of contribution of a case should be high if the semantic range of the example case fillers in that case is diverse in the case frame (see figure 3) . Let a certain verb have n senses (sl, 32,..., s~) and the set of example case fillers of a case c associated with 3~ be $3~,c. Then, the degree of c's contribution to disambiguation, CCD(c), is expected to be higher if the example case filler sets {\u00a3si,c I i = 1,..., n} share less elements. This can be realized by equation 6.", | |
| "cite_spans": [ | |
| { | |
| "start": 298, | |
| "end": 314, | |
| "text": "(sl, 32,..., s~)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 253, | |
| "end": 262, | |
| "text": "figure 3)", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "CCD( ) = 1 I&.d + I&j, l - n &j, l i=1 j=i+t", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "(6) a is the constant for parameterizing to what extent CCD influences verb sense disambiguation. When a is larger, CCD more strongly influences the system's output. Considering the data sparseness problem, we do not distinguish two nonns X and Y in equation 6if X and Y are similar enough, as in equation 7.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "{X} + {Y} = {X} if 3im(X,Y) >= 9 (7)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Relative strength of selectional restriction (RSSR) is computed in the following way. Tile selectional restriction on a ease of a case frame is expected to be strong if the example case fillers of tile case are similar to each ()tiler. Given a set of example case fillers ill a case associated with a verb sense, the strength of the selectional restriction on that case (SSR) can be estimated by averaging the similarity between any combination of two elements of that set. Thus, given a set Es,c of example case fillers in a case c associated with a verb sense s, tile SSR of c associated with s Call be estimated by equation 8 ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "E =I Ej=++, SSR(s, c) = ,+C2 if m > 1 maximum otherwise", | |
| "eq_num": "(8)" | |
| } | |
| ], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In the case m = 1, that is, the case has only one example case filler, tile SSR becomes maxinmm, because the selectional constraint associated with the case is highest (following table 1, we assign 11", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "as the maximum to SSR). The relative strength of selectional restriction (RSSR) of a case associated with a verb sense is estimated by the ratio of tile SSR of tile case to the summation of the SSRs of each case associated with the verb sense, as in equation (9) 4ssR(. , ,0 (9) a Evahmtion", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Our experiment compared the performance of the following methods:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "1. tOlrohashi's method: equation 12. our method (considering CCD): equation 33. our method (considering /)oth CCD and RSSR): equation 4In method 2 and 3, the influence of CCD, i.e. (~ in equation 6, was extremely large. We will show the relation between the w~riation of c~ and tile performance of the system later in this section. The training/test data used in tile ext)eriment contained over one thousand simple Japanese sentences collected from slews articles. The examples given by IPAL were also used as training data s. !),ach of tile sentences in the training/test data used in our experiment consisted of one or more complement(s) followed by one of the ten verbs enumerated in table 2. For each of the ten verbs, we conducted six-fold cross validation; that is, we divided the training/test data into six equal parts, and conducted six trials in each of which a different one of the six parts was used as test data and the rest was used as training data. We shall call the former the \"test set\" and the latter the \"training set,\" in each (:ase.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "When inore than one interpretation of an input verb is assigned the highest t)lausibility score, any of the above methods will (;hoose as its outt)ut the one that appears most frequently in the training data. Therefore, tile applicability in each method is 100%, given that the applicability is tile ratio of the number of the cases where the system Rives only one intert)retation, to the numt)er of inputs. Thus, in tile ext)eriment, we compared the precision of each method, which is in our case equal to the ratio of the nuinber of correct outputs, to tile nulnt)er of int)uts.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Since tile 1)erformance of any corpus-based method depends on the size of training data, we tirst investigated how the precision of each method was improved as the training data increased. In this, we initially used only the examples given by IPAL, and progressively increased the size of the training data used, by considering an extra part of the training set (five parts of the total six data portions used) at each iteration, until finally taking all five l)arts in the training of our system. 4Note that., in equation 5, while SIM is an integer, PlSSI/. ranges in its value h'om 0 to 1. Therefore, II, SSI{, is influential only when several verb senses take the same value of SIM for a given ease.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "'~The number of examples given by IPAL was, on ~verage, :1.7 for each ease of each case frame.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The results are shown in figure 5, in which the x-axis denotes the ratio of the data used froln the training set, to tile total size of the training set. What can be derived fl'om figure 5 are the following. First, as more training data was considered, tile precision got higher for each method. Second, tile consideration of CCD, i.e. contribution of case. to verb sense disambiguation, improved on Kurohashi's method regardless of tile size of training data. (liven the whole training set, the precision improved from 75.2% to 82.4% (7.2% gain). Third, the introduction of the notion of RSSR did not fltrther improve on the inethod using only CCD. Table 2 shows tile performance for each verb on using the whole training set. The column of \"lower bound\" denotes tile precision gained in a naive method such that the system always chooses tile interpretation most frequently al)pearing in the training data (Gale et al., 1992) . Tile column of \"two highest CCD\" gives the two highest CCD values from the cases for each verb, which are calculated using whole training set.", | |
| "cite_spans": [ | |
| { | |
| "start": 908, | |
| "end": 927, | |
| "text": "(Gale et al., 1992)", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 650, | |
| "end": 657, | |
| "text": "Table 2", | |
| "ref_id": "TABREF3" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "4", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "I i ' i J ! i --4 8O 65 j..'\" ; .... i CCD -~ = CCD'~RSSR", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "I", | |
| "sec_num": "85" | |
| }, | |
| { | |
| "text": "Finally, let us see to what extent we should al-. low CCD to influence verb sense disambiguation. Figure 6 shows the performance with the parametric constant ~ in equation (6) set to w~rious values. c~ = (/ corresponds with Kurohashi's method, in which CCD is never considered. As shown in figure 6 , the stronger influence we allow CCD to have, the better performance we gain.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 98, | |
| "end": 106, | |
| "text": "Figure 6", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 290, | |
| "end": 298, | |
| "text": "figure 6", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "55--", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In this paper, we proposed a slew example-based method for verb sense (tisambiguation, which lint)roved the performance of the existing method by considering the degree of contribution of case to verb sense disambigu~tion.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The performance of our method significantly depends on the method of assigning degree of similarity to a t)air of case fillers. Since Bunr'i~itloihyou is fundamentally based on human intuition, it does not reflect the similarity between a pair of case fillers computationaly. Proposed methods ....... ........... ............ .............. ...................... i ............... ~z9 ....... zo .............. ....... ; .................. ................ .................. .................. zz ! ....... ", | |
| "cite_spans": [ | |
| { | |
| "start": 293, | |
| "end": 508, | |
| "text": "....... ........... ............ .............. ...................... i ............... ~z9 ....... zo .............. ....... ; .................. ................ .................. .................. zz ! .......", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Figure 6: The relation between the degree of CCD and 1)recision of word clustering (Tokunaga et al., 1995, etc.) can 1)otentially be used ill conjunction with our method to overcome this human reliance.", | |
| "cite_spans": [ | |
| { | |
| "start": 83, | |
| "end": 112, | |
| "text": "(Tokunaga et al., 1995, etc.)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "5 115 225 30", | |
| "sec_num": "0" | |
| }, | |
| { | |
| "text": "In our current implenmntation, we consider the collocation between case fillers and verbs, but ignore the combination of case fillers. Instead of a database as in figure 1, we could store a set of combinations of example case fillers, e.g. the combination of s~wi (\"pickpocket\") and saifu (\"wallet\"), but not that of suri and otoko (\"man\"). Itowever, this way of data storage would require the collection of a much larger number of examples than the current method. This issue needs to be fl~rther investigated.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "5 115 225 30", | |
| "sec_num": "0" | |
| }, | |
| { | |
| "text": "2g's2,ca is not taken into consideration in the com-put~ttion since ca does not ~H)pe~tr in tile input.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "The authors would like to thank Dr. Manaim Okumura (JAIST, Japan), Dr. Michael Zock (LIMSI, France) and Mr. Timothy Baldwin (TITech, Jat)an) for their comments on the earlier version of this paper.", | |
| "cite_spans": [], | |
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| "section": "Acknowledgments", | |
| "sec_num": null | |
| } | |
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| "FIGREF0": { | |
| "text": "Figure 2ilhlstrates a fragment of B'unruigoihyo in(:hlding some of the nouns in figure 1. I,et us take the example sentence (1).", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "text": "A fragment of Bunruigoihyo accusative to verb sense disambiguation is greater than that of the nominative with the case of verb ~t(-)ru. 1'", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF3": { | |
| "text": "An inl)uL aud Lhe database in the course of tlle verb sense disanll)iguation process, the system tirst discards the candidates whose case Dame coi~straint is grammatically violated by the input (this parallels Kurohashi's method). Ill the c}lse of figure 4, .s:) is dist:arded bec3.use the ('.&se fl'~Li[ie of v (,s3) does ilOt su])", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "text": ", where \u00a3~,c is an i4h element of \u00a33,c, and m is the number of elements in \u00a3s,c, i.e. m = [$3,c[.", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF6": { | |
| "text": "The precision of each method, for each size of training data", | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "TABREF2": { | |
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| "html": null, | |
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| "TABREF3": { | |
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| "content": "<table><tr><td/><td/><td>data</td><td># of</td><td>lower</td><td/><td/><td colspan=\"2\">precision (%)</td></tr><tr><td/><td>wn'b</td><td>size</td><td>candidates</td><td>bound (%)</td><td colspan=\"2\">two highest COl)</td><td/></tr><tr><td/><td>ataer~t</td><td>136</td><td>4</td><td>66.9</td><td>o (0.98)</td><td>0a (0.86)</td><td>77.2</td><td>80.0</td></tr><tr><td/><td>kakeru</td><td>160</td><td>29</td><td>25.6</td><td>o (0.99)</td><td>ni (9.98)</td><td>66.3</td><td>76.9</td></tr><tr><td/><td>kztwa, eru</td><td>107</td><td>5</td><td>53.9</td><td>o (0.98)</td><td>ni (0.95)</td><td>82.6</td><td>88.0</td></tr><tr><td/><td>n o'r~t</td><td>126</td><td>I O</td><td>45.2</td><td>',~i (0.90)</td><td>0\" (0.9'))</td><td>82.5</td><td>81.0</td></tr><tr><td/><td>osamcr'u</td><td>108</td><td>8</td><td>25.0</td><td>o (0.95)</td><td>ni (0.94)</td><td>73.2</td><td>70.4</td></tr><tr><td/><td>tsul,'wrn</td><td>12(';</td><td>15</td><td>19.8</td><td>de (1.0)</td><td>o (0.98)</td><td>59.2</td><td>84.9</td></tr><tr><td/><td>to*'~l</td><td>84</td><td>29</td><td>26.2</td><td colspan=\"2\">kara (1.O) o (0.99)</td><td>56.0</td><td>71.4</td></tr><tr><td/><td>~n~u</td><td>90</td><td>2</td><td>81.1</td><td>o (1.O)</td><td>ga (0.94)</td><td>100</td><td>98.9</td></tr><tr><td/><td>wokaru</td><td>60</td><td>5</td><td>48.3</td><td>~(* (0.96)</td><td>,~i (o.ro)</td><td>05.0</td><td>70,O\"</td></tr><tr><td/><td>ya'm, ertt</td><td>54</td><td>2</td><td>59.3</td><td>o (1.0)</td><td>de (0.71)</td><td>96.3</td><td>96.3</td></tr><tr><td/><td>tottd</td><td>l 1 t1111</td><td/><td>43,7</td><td/><td/><td>r5.2</td><td>I 82.4_~</td></tr><tr><td>83</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>so</td><td>.</td><td/><td/><td/><td/><td/><td/></tr></table>", | |
| "text": "Performance for each verb (ga: nominative, ni: dative, o: accusative, kava: locative, de: instrumental)", | |
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