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. M., E., F., E., M., Word sense discrimination on tweets: A graph-based approach, Paper, in IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, (Lisbon, Portugal, 12-14 November 2015), SciTePress, Setúbal 2015:1 138-146. 10.5220/0005640501380146 [http://hdl.handle.net/10807/122106]
Word sense discrimination on tweets: A graph-based approach
Cecchini, Flavio Massimiliano
;
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
