This paper presents a new clustering algorithm called DSCBC which is designed to automatically discover word senses for polysemous words. DSCBC is an extension of CBC (Pantel and Lin, 2002), and incorporates feature domain similarity: the similarity between the features themselves, obtained a priori from sources external to the dataset. By incorporating the feature domain similarity in clusteri...

We analyze the results of a semantic annotation task performed by novice taggers as part of the WordNet SemCor project (Landes et al., in press). Each polysemous content word in a text was matched to a sense from WordNet. Comparing the performance of the novice taggers with that of experienced lexicographers, we find that the degree of polysemy, part of speech, and the position within the WordN...

Polysemous Words can have more than one distinct meaning. Word sense disambiguation (WSD) is the ability to identify the exact meaning of such polysemous words in context in a computational manner. WSD is considered as an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problem in Artificial Intelligence. In this paper, we propose an Integrated Kanna...

The Academic Vocabulary List (AVL) (Gardner & Davies, 2014) is a valuable resource for EAP teachers and students as it identifies potential lexical learning/teaching targets. This study enhances the AVL's pedagogical usefulness by identifying polysemous lemmas in it. Polysemous AVL are operationalised those with more than one definition two lexicographic resources, Collins COBUILD Advanced Lear...

 Lexicalization-patterns, the way words are mapped onto concepts, differ from one language      to another. This study investigated the influence of first language (L1) lexicalization patterns on the processing of second language (L2) words in sentential contexts by both less proficient and more proficient Persian learners of English. The focus was on cases where two different senses of a polys...

A word in natural language can be polysemous, having multiple meanings, as well as synonymous, meaning the same thing as other words. Word sense induction attempts to find the senses of polysemous words. Synonymy detection attempts to find when two words are interchangeable. We combine these tasks, first inducing word senses and then detecting similar senses to form word-sense synonym sets (syn...

In a lexical-decision task (LDT), Hino and Lupker (1996) reported a polysemy effect (faster response times for polysemous words [e.g., BANK]), and attributed this effect to enhanced feedback from the semantic system to orthographic units, for polysemous words. Using the same task, Pexman, Lupker, and Jared (in review) reported a homophone effect (slower response times for homophonic words [e.g....