We propose a supervised word sense disambiguation (WSD) system that uses features obtained from clustering results of word instances. Our approach is novel in that we employ semi-supervised clustering that controls the fluctuation of the centroid of a cluster, and we select seed instances by considering the frequency distribution of word senses and exclude outliers when we introduce “must-link”...

Clustering coefficient-a measure derived from the new science of networks-refers to the proportion of phonological neighbors of a target word that are also neighbors of each other. Consider the words bat, hat, and can, all of which are neighbors of the word cat; the words bat and hat are also neighbors of each other. In a perceptual identification task, words with a low clustering coefficient (...

Sense induction seeks to automatically identify word senses of polysemous words encountered in a corpus. Unsupervised word sense induction can be viewed as a clustering problem. In this paper, we used the Hierarchical Clustering Algorithm as the classifier for word sense induction. Experiments show the system can achieve 72% F-score about train-corpus and 65% F-score about test-corpus.

We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves c...

Word sense induction (WSI) seeks to induce senses of words from unannotated corpora. In this paper, we address the WSI task for the Croatian language. We adopt the word clustering approach based on co-occurrence graphs, in which senses are taken to correspond to strongly inter-connected components of co-occurring words. We experiment with a number of graph construction techniques and clustering...

In this paper, we propose a novel multi-document summarization strategy based on Basic Element (BE) vector clustering. In this strategy, sentences are represented by BE vectors instead of word or term vectors before clustering. BE is a head-modifier-relation triple representation of sentence content, and it is more precise to use BE as semantic unit than to use word. The BE-vector clustering is...

Abstract: In this work clustering performance has been increased by proposes an algorithm called constrained informationtheoretic co-clustering (CITCC). In this work mainly focus on co-clustering and constrained clustering. Co-clustering method is differing from clustering methods it examine both document and word at a same time. A novel constrained coclustering approach proposed that automatic...

Most distributional models of word similarity represent a word type by a single vector of contextual features, even though, words commonly have more than one sense. The multiple senses can be captured by employing several vectors per word in a multi-prototype distributional model, prototypes that can be obtained by first constructing all the context vectors for the word and then clustering simi...