The SENSEVAL-3 Multilingual English-Hindi Lexical Sample Task

This paper describes the English-Hindi Multilingual lexical sample task in SENSEVAL-3

Discriminating Among Word Senses Using McQuitty’s Similarity Analysis

This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in similar contexts are grouped together via McQuitty’s Similarity Analysis, an agglomerative clustering algorithm. The context in which a target word occurs is represented by surface lexical features such as unigrams, bigrams, and second order co-occurrences. This paper summarizes our approach, and describes the results of a preliminary evaluation we have carried out using data from the SENSEVAL-2 English lexical sample and the line corpus

Discriminating Among Word Meanings By Identifying Similar Contexts

Word sense discrimination is an unsupervised clustering problem, which seeks to discover which instances of a word/s are used in the same meaning. This is done strictly based on information found in raw corpora, without using any sense tagged text or other existing knowledge sources. Our particular focus is to systematically compare the efficacy of a range of lexical features, context representations, and clustering algorithms when applied to this problem

The SENSEVAL-3 Multilingual English-Hindi Lexical Sample Task

This paper describes the English--Hindi Multilingual lexical sample task in SENSEVAL--3. Rather than tagging an English word with a sense from an English dictionary, this task seeks to assign the most appropriate Hindi translation to an ambiguous target word. Training data was solicited via the Open Mind Word Expert (OMWE) from Web users who are fluent in English and Hindi

Comparing linguistic features for modeling learning in computer dialogue tutoring

Abstract. We compare the relative utility of different automatically computabl