A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model

A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning...

Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning...

Distributed representation-based spoken word sense induction

Spoken Term Detection (STD) or Keyword Search (KWS) techniques can locate keyword instances but do not differentiate between meanings. Spoken Word Sense Induction (SWSI) differentiates target instances by clustering according to context, providing a more useful result. In this paper we present a fully unsupervised SWSI approach based on distributed representations of spoken utterances. We compa...

Word Sense Disambiguation – A Context based Multimode WSD Model

For many decades researchers in the domain of NLP (Natural Language Processing) and its applications like Machine Translation, Text Mining, Question Answering, Information Extraction and Information retrieval etc. have been posed with a challenging area of research i.e. WSD (Word Sense Disambiguation) WSD can be defined as the ability to correctly ascertain the meaning of a word, with reference...

Word Sense Disambiguation Based on Word Sense Clustering