In this paper we are going to detail an unsupervised, graph-based approach for word sense discrimination on tweets. We deal with this problem by constructing a word graph of co-occurrences. By defining a distance on this graph, we obtain a word metric space, on which we can apply an aggregative algorithm for word clustering. As a result, we will get word clusters representing contexts that discriminate the possible senses of a term. We present some experimental results both on a data set consisting of tweets we collected and on the data set of task 14 at SemEval-2010.
Cecchini, F., Fersini, E., Messina, V. (2015). Word sense discrimination on tweets: A graph-based approach. In IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon; Portugal; 12-14 November 2015 (pp.138-146). SciTePress [10.5220/0005640501380146].
Word sense discrimination on tweets: A graph-based approach
CECCHINI, FLAVIO MASSIMILIANOPrimo
;FERSINI, ELISABETTASecondo
;MESSINA, VINCENZINAUltimo
2015
Abstract
In this paper we are going to detail an unsupervised, graph-based approach for word sense discrimination on tweets. We deal with this problem by constructing a word graph of co-occurrences. By defining a distance on this graph, we obtain a word metric space, on which we can apply an aggregative algorithm for word clustering. As a result, we will get word clusters representing contexts that discriminate the possible senses of a term. We present some experimental results both on a data set consisting of tweets we collected and on the data set of task 14 at SemEval-2010.
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