Toward Disambiguating Typed Predicate-Argument Structures for Italian
Authors: Octavian Popescu, Ngoc Phuoc an Vo, Anna Feltracco, Elisabetta Jezek, Bernardo Magnini (Università di Pavia, Fondazione Bruno Kessler, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
We report a word sense dis-ambiguation experiment on Italian verbs where both the sense inventory and the training data are derived from T-PAS, a lex-ical resource of typed predicate-argument structures grounded on corpora. We present a probabilistic model for sense dis-ambiguation that exploits the semantic fea-tures associated to each argument position of a verb.

Introduction
Word Sense Disambiguation (WSD) (see (Agirre and Edmonds, 2006) for a comprehensive survey of the topic) is a task in Computational Linguistics where a system has to automatically select the cor-rect sense of a target word in context, given a list of possible senses for it. For instance, given the target word chair in the context of the sentence The cat is on the chair, and given two possible senses for the word, let’s call them chair as forniture and chair as human, a WSD system should be able to select the first sense as the appropriate one. An important aspect of WSD is that its complexity is affected by the ambiguity (i.e. the number of senses) of the words to be disambiguated. This has led in the past to discussing various characteristics of available sense repositories (e.g. WordNet, Fell-baum 1998), including the nature and the number of sense distinctions, particularly with respect to the application goals of WSD. In this paper we address Word Sense Disam-biguation of Italian verbs. Differently form pre-vious work on WSD for Italian (Bertagna et al. 2007), where the sense repository was ItalWordNet (Roventini et al. 2003), in our experiments we use verb senses derived from T-PAS, a repository of Typed Predicate Argument Structures for Italian acquired from corpora. There are two benefits of this choice: (i) word sense distinctions are now grounded on actual sense occurrences in corpora, this way ensuring a natural selection with respect of sense granularity; (ii) as in T-PAS for each verb sense a number of sentences are collected, there is no further need to annotate data for training and testing, avoiding the issue of re-interpreting sense distinctions by different people. The paper is organized as follows. Section 2 introduces T-PAS, including the methodology for its acquisition. Section 3 presents the probabilistic model that we have used for verb disambiguation and Section 4 reports on experimental results.

Conclusion
We have presented a word sense disambiguation system for Italian verbs, whose senses have been derived from T-PAS, a lexical resource that we have recently developed. This is the first work (we hope that many others will follow) attempting to use T-PAS for a NLP task. The WSD system takes advantage of the T-PAS structure, particularly the presence of semantic types for each verbal argument position. Results, although preliminary, show a very good precision. As for the future, we intend to consolidate the disambiguation methodology and we aim at a more detailed annotation of the sentence argument, corresponding to the internal structure of verb patterns. We plan to extend the analysis of the relationship between senses of the different positions in a pattern in order to implement a metrics based on tree and also to substitute the role of the parser with an independent pattern matching system. The probabilistic model presented in Section 3 can be extended in order to determine also the probability that a certain word is a syntactic head.