Discovering word senses from a network of lexical cooccurrences

Discovering word senses from a network of lexical cooccurrences Olivier Ferret CEA — LIST/LIC2M 18, route du Panorama 92265 Fontenay-aux-Roses, France ferreto@zoe.cea.fr Abstract Lexico-semantic networks such as WordNet have been criticized about the nature of the senses they distinguish as well as on the way they define these senses. In this article, we pre- sent a possible solution to overcome these lim- its by defining the sense of words from the way they are used. More precisely, we propose to differentiate the senses of a word from a net- work of lexical cooccurrences built from a large corpus. This method was tested both for French and English and was evaluated for Eng- lish by comparing its results with WordNet. 1 Introduction Semantic resources have proved to be useful in information retrieval and information extraction for applications such as query expansion (Voor- hees, 1998), text summarization (Harabagiu and Maiorano, 2002) or question/answering (Pasca and Harabagiu, 2001). But this work has also shown that these resources must be used with caution: they bring on an improvement of results only if word sense disambiguation is performed with a great accuracy. These findings bring one of the first roles of a semantic resource to light: discrimi- nating and characterizing the senses of a set of words. The main semantic resources with a wide coverage that can be exploited by computers are lexico-semantic networks such as WordNet. Be- cause of the way they were built, mainly by hand, these networks are not fundamentally different from traditional dictionaries. Hence, it is not very surprising that they were criticized, as in (Hara- bagiu et al., 1999), for not being suitable for Natu- ral Language Processing. They were criticized both about the nature of the senses they discrimi- nate and the way they characterize them. Their senses are considered as too fine-grained but also incomplete. Moreover, they are generally defined through their relations with synonyms, hyponyms and hyperonyms but not by elements that describe the contexts in which they occur. One of the solutions for solving this problem consists in automatically discovering the senses of words from corpora. Each sense is defined by a list of words that is not restricted to synonyms or hy- peronyms. The work done in this area can be di- vided into three main trends. The first one, repre- sented by (Pantel and Lin, 2002), is not focused on the problem of discovering word senses: its main objective is to build classes of equivalent words from a distributionalist viewpoint, hence to gather words that are mainly synonyms. In the case of (Pantel and Lin, 2002), the discovering of word senses is a side effect of the clustering algorithm, Cluster By Committee, used for building classes of words: as a word can belong to several classes, each of them can be considered as one of its senses. The second main trend, found in (Schütze, 1998), (Pedersen and Bruce, 1997) and (Puran- dare, 2003), represents each instance of a target word by a set of …

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