Parsing the Voyager Domain Using Pearl

Parsing the Voyager Domain Using Pearl David M. Magerman and Mitchell P. Marcus CIS Department University of Pennsylvania Philadelphia, PA 19104 ABSTRACT This paper* describes a .al, ural language p~rsi.g algorithm &)r un- resu'icl,ed I,ext which uses a probabilhq-hased scoring funct,iow I,o se- le(:l, I,he "besC: parse of ~/ sent,enos acc:ording t,c~ a given gra0nunar. The parser~ "Pearl~ ix a i,hne-asynciironous t)ol,l,orn-u 1) chart, parser with Earley-l,ype I,Ol)-dowil t)redic:l,ion which pursues I,he }6gtmsl,-s(:ori.g t,he- ory in I,he (:|larl,, where I,he set)re of a IJmory represe.l,s I,|le exl,e.l, I,o W|lic:|l L}le ~:on|,exl, ()~ I,|le seutl,euic:e predic:l;s I,}lat, int,erprel,at,ion. This parser {lifters front previous au,eUnl)l,s el, sto(:}la.st,i(: parsers in I,|lal, it, .ses a richer h)rm of condii,ional probabilil, ies |)a.~ed on conl,exl, l,o predict, likelihood. Pearl Mso provides a framework for in(:orporat,ing the resuh,s of previo.s work in part,-of-st)eeeh assig.tnenl,) InlkutowIi word u||od- els, and olJher l)rol)abilist,ic models (ff linguistfic feauJres im,o o.e pars- i.g U)t)], inl,erleaving I,}lese I,e(:imiques |.stead of usi.g I, he I,ra~lil,iona] pipeline archil,eci, ure. In I, esl~ perh~ruvled on I,|ie Voyager (lirecl,io.- finding domain, "Pearl has been s.ccessful el, resolvi.g parl,-of-speech aunifiguhq, del,ermiufing cal,egories for umknow, words, and selecl,ing correcl, parses firsl, using a very loosely fil, l,lng covering grammar. ~ INTRODUCTION All natural language grammars are ambiguous. Even tightly fitting natural language grammars are ambiguous in some ways. Loosely fitting grammars, which are necessary for handhng the variability and complexity of unrestricted text and speech, are worse. The standard technique for dealing with this ambiguity, prtming grammars by hand, is painful, time-consuming, and usu- ally arbitrary. The solution which many people have proposed is to use stochastic models to train statistical grammars automati- cally from a large corpus. Attempts in applying statistical techniques to natural lan- guage parsing have exhibited varying degrees of success. These successful and unsuccessful attempts have suggested to us that: • Stochastic techniques combined with traditional hnguistic theories can (and indeed must) provide a solution to the natural language understanding problem. *This work was partially supportcd by DARPA grant No. Nt1014-85- K0018, ONR contract No. Ntltltl14-89-O~0171 by DARPA and AIrOSR jointly ttndcr grant No. AFOSR-90-Ut166, and by ARO grant No. DAAL 03-89-Ctlt131 PRL SpccJal thanks to Carl Wcir and Lynettc Hirschman at Unisys for thcir valucd input, guidancc and support. 2Thc grammar uscd for o~tr cxpcrimcnts is the string grammar used in U nisys: P U NDIT natural languagc undcrstanding systcm. • In order for stochastic techniques to be ett~ctive, they must be applied with restraint (poor estimates of context axe worse than none[6|). • Interactive, interlea~'ed architectures axe preferable to pipeline architectures in NLU systems, because they use more of the available information in the decision-malting process. We have constructed a stochastic parser, "Pearl, which is based on these ideas. The development of the Pearl parser is an ettbrt to combine the statistical models developed recently into a single tool which incorporates all of these models into the decision-making compo- nent of a. parser. While we hax'e only attempted to incorporate a …

Content truncated to 500 words