Combining Distributional Semantic Models and Sense Distribution for Effective Italian Word Sense Disambiguation
Authors: Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro (Università di Bari Aldo Moro, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Distributional semantics ap-proaches have proven their ability to en-hance the performance of overlap-based Word Sense Disambiguation algorithms. This paper shows the application of such a technique to the Italian language, by analysing the usage of two different Dis-tributional Semantic Models built upon ItWaC and Wikipedia corpora, in con-junction with two different functions for leveraging the sense distributions. Results of the experimental evaluation show that the proposed method outperforms both the most frequent sense baseline and other state-of-the-art systems.

Introduction
Given two words to disambiguate, Lesk (1986) al-gorithm selects those senses which maximise the overlap between their definitions (i.e. glosses), then resulting in a pairwise comparison between all the involved glosses. Since its original formula-tion, several variations of this algorithm have been proposed in an attempt of reducing its complex-ity, like the simplified Lesk (Kilgarriff and Rosen-zweig, 2000; Vasilescu et al., 2004), or maximizing the chance of overlap, like in the adapted version (Banerjee and Pedersen, 2002). One of the limitations of Lesk approach relies on the exact match between words in the sense definitions. Semantic similarity, rather than word overlap, has been proposed as a method to overcome such a limitation. Earlier approaches were based on the notion of semantic relatedness (Patwardhan et al., 2003) and tried to exploit the relationships between synsets in the WordNet graph. More recently, Distributional Semantic Models (DSM) have stood up as a way for computing such semantic similarity. DSM allow the representation of concepts in a geometrical space through word vectors. This kind of representation captures the semantic relatedness that occurs between words in paradigmatic relations, and enables the computation of semantic similarity between whole sentences. Broadening the definition of semantic relatedness, Patwardhan and Pedersen (2006) took into account WordNet contexts: a gloss vector is built for each word sense using its definition and those of related synsets in WordNet. A distributional thesaurus is used for the expansion of both glosses and the context in Miller et al. (2012), where the overlap is computed as in the original Lesk algorithm. More recently, Basile et al. (2014) proposed a variation of Lesk algorithm based on both the simplified and the adapted version. This method combines the enhanced overlap, given by the definitions of related synsets, with the reduced number of matching that are limited to the contextual words in the simplified version. The evaluation was conducted on the SemEval-2013 Multilingual Word Sense Disambiguation task (Navigli et al., 2013), and involved the use of BabelNet (Navigli and Ponzetto, 2012) as sense inventory. While performance for the English task was above the other task participants, the same behaviour was not reported for the Italian language. This paper proposes a deeper investigation of the algorithm described in Basile et al. (2014) for the Italian language. We analyse the effect on the disambiguation performance of the use of two different corpora for building the distributional space. Moreover, we introduce a new sense distribution function (SDfreq), based on synset frequency, and compare its capability in boosting the distributional Lesk algorithm with respect to the one proposed in Basile et al. (2014). The rest of the paper is structured as follows: Section 2 provides details about the Distributional Lesk algorithm and DSM, and defines the two above mentioned sense distribution functions exploited in this work. The evaluation, along with details about the two corpora and how the DSM are built, is presented in Section 3, which is followed by some conclusions about the presented results.

Conclusion
This paper proposed an analysis for the Italian language of anenhanced version of Lesk algorithm, which replaces the word overlap with distributional similarity. We analysed two DSM built over the ItWaC and Wikipedia corpus along with two sense distribution functions (SDprob and SDfreq). The sense distribution functions were computed over MultiSemCor, in order to avoid missing references between Italian and English synsets. The combination of the ItWaC-based DSM with the SDprob function resulted in the best overall result for the Italian portion of SemEvalTask-12 dataset.