Probabilistic models for PP-attachment resolution and NP analysis

Probabilistic Models for PP-attachment Resolution and NP Analysis Eric Gaussier XRCE 6, Chemin de Maupertuis 38240 Meylan, France fname.lname@xrce.xerox.com Nicola Cancedda XRCE 6, Chemin de Maupertuis 38240 Meylan, France fname.lname@xrce.xerox.com Abstract We present a general model for PP attach- ment resolution and NP analysis in French. We make explicit the different assumptions our model relies on, and show how it gener- alizes previously proposed models. We then present a series of experiments conducted on a corpus of newspaper articles, and as- sess the various components of the model, as well as the different information sources used. 1 Introduction Prepositional phrase attachment resolution and noun phrase analysis are known to be two difficult tasks. Traditional context-free rules for example do not help at all in selecting the good parse for a noun phrase, since all valid parses are a priori correct. Subcatego- rization information can help solve the problem, but the amount of information necessary to be encoded in such lexicons is huge, since in addition to subcat- egorization frames, one should encode the different senses of words and rules on how these senses can be combined, as well as the different units (single or multi word expressions) a language makes use of. It has been recognized in different works that part of this knowledge can be (semi-)automatically acquired from corpora and used in the decision process. Several models have been proposed for PP-attachment resolu- tion and NP analysis, built on various building blocks and making use of diverse information. Most of these models fit within a probabilistic framework. Such a framework allows one to estimate various quanti- ties and to perform inference with incomplete infor- mation, two necessary steps for the problem at hand. We present in this paper a model generalizing several models already proposed and integrating several infor- mation sources. We focus here on analysing strings corresponding to a combination of traditional VERB NOUN PREP sequences and noun phrases. More precisely, the NPs we consider are arbitrarily complex noun phrases with no embedded subordinate clause. The PP attachment problems we tackle are those involved in analysing such NPs for languages relying on composition of Ro- mance type (as French or Italian), as well as those present in verbal configurations for languages relying on composition of Germanic type (as German or En- glish). However, our model can easily be extended to deal with other cases as well. The problem raised in analysing such sequences is of the utmost relevance for at least two reasons: 1) NPs constitute the vast ma- jority of terms. Correctly identifying the boundaries and the internal structure of noun phrases is crucial to the automatic discovery of domain-specific termino- logical databases; 2) PP-attachment ambiguity is one of the main sources of syntactic ambiguity in general. In the present work, we focus on the French lan- guage since we have various lexical information at our disposal for this language that we would like to as- sess in the given context. Furthermore, French dis- plays interesting propoerties …

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