Word Sense Clustering and Clusterability

Word Sense Clustering and Clusterability

Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we a...

Word Sense Disambiguation Based on Word Sense Clustering

Clustering Paraphrases by Word Sense

Automatically generated databases of English paraphrases have the drawback that they return a single list of paraphrases for an input word or phrase. This means that all senses of polysemous words are grouped together, unlike WordNet which partitions different senses into separate synsets. We present a new method for clustering paraphrases by word sense, and apply it to the Paraphrase Database ...

Computational Feasibility of Clustering under Clusterability Assumptions

It is well known that most of the common clustering objectives are NPhard to optimize. In practice, however, clustering is being routinely carried out. One approach for providing theoretical understanding of this seeming discrepancy is to come up with notions of clusterability that distinguish realistically interesting input data from worst-case data sets. The hope is that there will be cluster...

LIMSI : Cross-lingual Word Sense Disambiguation using Translation Sense Clustering

We describe the LIMSI system for the SemEval-2013 Cross-lingual Word Sense Disambiguation (CLWSD) task. Word senses are represented by means of translation clusters in different languages built by a cross-lingual Word Sense Induction (WSI) method. Our CLWSD classifier exploits the WSI output for selecting appropriate translations for target words in context. We present the design of the system ...