A Statistical Approach to Sense Disambiguation in Machine Translation

A Statistical Approach to Sense Disambiguation in Machine Translation Peter F. Brown, Stepheu A. Della. Pietra, Vince,,t d. Della Pietra, Robert L. Mercer • -' S IBM Resea,rch Division, ql horns,: J. Wa, tson Resea,rch Center Yorktown Heights, NY 10598 ABSTRACT We describe ~ statisticM technique for assigning senses to words. An instance of ~ word is assigned ;~ sense by asking a question about the context in which the word ~tppears. The qttestlou is constructed to ha, re high mutua,1 i~fformation with the word's translations. INTRODUCTION An a,lluring a,spect of the staMstica,1 a,pproa,ch to ins,chine tra,nsla,tion rejuvena.ted by Brown, et al., [_1] is the systems.tic framework it provides for a.tta.ck- ing the problem of lexicM dis~tmbigua.tion. For exam- ple, the system they describe tra,ns]a.tes th.e French sentence Je vais prendre la ddeision a,s [ will make the decision, thereby correctly interpreting prendre a.s make, The staMstica.l tra.nslation model, which sup- plies English. tra,nsla,tions of French words, prefers the more common tra.nslation take, but the trigram la.n- gu.age mode] recognizes tha.t the three-word sequence make the decision is much more proba])le tha.n take the decision. The system is not a.lwa,ys so successful. It incor- rectly renders Je vats prendre ma propre ddcision a.s 1 will take my own decision. Here, the la.nguage model does not realize tha, t take my own decision is improb- able beca,use take a,nd decision no longer fall within a. single trigram. Errors such a.s this a,re common because otlr sta,- tistical models o.ly capture loca,l phenomena,; if l, he context necessa,ry to determine ~ transla, tion fa,lls out- side the scope of our models, the word is likely to be tra,nsla,ted incorrectly. However, if the re]evant co.- text is encoded locally, the word should be tra, nsla, ted correctly. We ca,n a,chieve this within the traditionM p,~radigm of a.na,lysis - tra,nsfer - synthesis by incorpo- ra,ting into the ana,lysis pha,se a, sense--disa, mbigu~tion compo,ent that assigns sense la, bels to French words. ]if prendre is labeled with one sense in the context of ddcisiou but wil.h a, different sense in other contexts, then the tra,nsla,tion model will learn from training data tha,t the first sense usua,lly tra.nslates to make, where.a,s the other sense usua,lly tra.nslates to take. In this paper, we describe a. sta, tistica,1 procedure for constructing a. sense-disambiguation eomponent that label words so as to elucida.te their translations. STATISTICAL TRANSLATION As described by Brown, et al. []], in the sta.tistica.1 a.l)proa.ch to transla, tion, one chooses for tile tra,nsla,- tion of a. French sentence .F, tha.t English sentence E which ha.s the greatest l)robability, Pr(EIF), a.ccord- i,g to a, model of th.e tra, ns]ation process. By Ba.yes' r,,le, Pr(EI ~') = Pr(E) Pr(FIE )/Pr(.F). Since the (lenomina.tor does not del)end on E, the sentence for which Pr(EIF ) is grea, test is also the sentence for which the product Pr(E) Pr(FIE ) is grea~test. The first term in this product is a~ sta, tisticM cha.ra.cteriza- tion of the, English ]a.nguage a, nd the …

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