Word Sense Discrimination: A gangplank algorithm
Authors: Flavio Massimiliano Cecchini, Elisabetta Fersini (Università di Milano Bicocca, 2015)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper we present an unsupervised, graph-based approach for Word Sense Discrimination. Given a set of text sentences, a word co-occurrence graph is derived and a distance based on Jaccard index is defined on it; subsequently, the new distance is used to cluster the neighbour nodes of ambiguous terms using the concept of ``gangplanks'' as edges that separate denser regions (``islands'') in the graph. The proposed approach has been evaluated on a real data set, showing promising performance in Word Sense Discrimination.

Introduction
Word Sense Disambiguation is a challenging research task in Computational Linguistics and Natural Language Processing. The main reasons behind the difficulties of this task are ambiguity and arbitrariness of human language: just depending on its context, the same term can assume different interpretations, or senses, in an unpredictable manner. In the last decade, three main research directions have been investigated AUTHOR: 1) supervised AUTHOR, 2) knowledge-based AUTHOR and 3) unsupervised Word Sense Disambiguation AUTHOR, where the last approach is better defined as ``induction'' or ``discrimination''. In this paper we focus on the automatic discovery of senses from raw text, by pursuing an unsupervised Word Sense Discrimination paradigm. We are interested in the development of a method that can be generally independent from the register or the linguistic well-formedness of a text document, and, given an adequate pre-processing step, from language. Among the many unsupervised research directions, i.e. context clustering AUTHOR, word clustering AUTHOR, probabilistic clustering AUTHOR and co-occurrence graph clustering AUTHOR , we committed to the last one, based on the assumption that word co-occurrence graphs can reveal local structural properties tied to the different senses a word might assume in different contexts. Given a global word co-occurrence graph, the main goal is to exploit the subgraph induced by the neighbourhood of the word to be disambiguated (a ``word cloud''). There, we define separator edges (``gangplanks'') and use them as the means to cluster the word cloud: the fundamental assumption is that, in the end, every cluster will correspond to a different sense of the word. The paper is organized as follows. In section grafigrafigrafi we explain how we build our co-occurrence graph and word clouds by means of a weighted Jaccard distance. In section clasteri we describe the gangplank algorithm. In section tuitti we present the algorithm's results on our data set and their evaluation. In section parenti we give a brief overview on related work and section fine presents some short conclusions.

Conclusion
The main challenge we encountered for our word sense discrimination algorithm was the difficulty of handling a small-world graph. Apart from that, we have to notice that word clustering just represents the last step of a process that starts with pre-processing and tokenization of a text, which are both mostly of supervised nature. Our future goals will be to investigate the relations between text pre-processing and clustering results, and how to render the whole process completely unsupervised.