IRST-BP: Preposition Disambiguation based on Chain Clarifying Relationships Contexts

Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 191–194, Prague, June 2007. c⃝2007 Association for Computational Linguistics IRST-BP: Preposition Disambiguation based on Chain Clarifying Relationships Contexts Octavian Popescu FBK-IRST, Trento (Italy) popescu@itc.it Sara Tonelli FBK-IRST, Trento (Italy) satonelli@itc.it Emanuele Pianta FBK-IRST, Trento (Italy) pianta@itc.it Abstract We are going to present a technique of preposition disambiguation based on sense discriminative patterns, which are acquired using a variant of Angluin’s al- gorithm. They represent the essential in- formation extracted from a particular type of local contexts we call Chain Clarifying Relationship contexts. The data set and the results we present are from the Semeval task, WSD of Preposi- tion (Litkowski 2007). 1 Introduction Word Sense Disambiguation (WSD) is a prob- lem of finding the relevant clues in a surround- ing context. Context is used with a wide scope in the NLP literature. However, there is a dichot- omy among two types of contexts, local and topical contexts (Leacock et. all 1993), that is general enough to encompass the whole notion and at the same to represent a relevant distinc- tion. The local context is formed by information on word order, distance and syntactic structure and it is not restricted to open-class words. A topical context is formed by the list of those words that are likely to co-occur with a particular sense of a word. Generally, the WSD methods have a marked predilection for topical context, with the consequence that structural clues are rarely, if ever, taken into account. However, it has been suggested (Stetina&Nagao 1997, Dekang 1997) that structural words, especially prepositions and particles, play an important role in computing the lexical preferences considered to be the most important clues for disambiguation. are Closed class words, prepositions in particular, ambiguous (Litkowski&Hargraves2006). Their disambiguation is essential for the correct processing of the meaning of a whole phrase. A wrong PP-attachment may render the sense of the whole sentence unintelligible. Consider for example: (1) Joe heard the gossip about you and me. (2) Bob rowed about his old car and his mother. A probabilistic context free grammar most likely will parse both (1) and (2) wrongly1. It would attach “about” to “to hear” in (1) and would consider the “his old car and his mother” the object of “about” in (2). The information needed for disambiguation of open class words is spread at all linguistics lev- els, from lexicon to pragmatics, and can be lo- cated within all discourse levels, from immedi- ate collocation to paragraphs (Stevenson&Wilks 1999). Intuitively, prepositions have a different behavior. Most likely, their senses are deter- mined within the government category of their 1 Indeed, Charniak’s parser, considered to be among the most accurate ones for English, parses wrongly both of them. 191 heads. We expect the local context to play the most important role in the disambiguation of prepositions. We are going to present a technique of prepo- sition disambiguation based on sense discrimina- tive patterns, which are acquired using a variant of Angluin’s algorithm. …

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