This paper presents a technique to improve the quality of Document Clustering based on Word Set Concept. The proposed Technique WDC (word set based document clustering), a clustering algorithm work with to obtain clustering of comparable quality significantly more efficiently more than the state of the art text clustering algorithm. The proposed WDC algorithms utilize the semantic relation ship...

This paper presents ACCWSI (Attentive Context Clustering WSI), a method for Word Sense Induction, suitable languages with limited resources. Pretrained on small corpus and given an ambiguous word (a query word) set of excerpts that contain it, uses attention mechanism generating context-aware embeddings, distinguishing between the different senses assigned to word. These embeddings are then clu...

Clustering methods have been extensively used in many Information Processing tasks in order to capture unknown object categories. However, clustering has been scarcely used as a sense labeling method for Word Sense Disambiguation (WSD), that is, as a way to identify groups of semantically related word senses that can be successfully used in a disambiguation process. In this paper, we present an...

In this paper we present a non-languagespecific strategy that uses large amounts of monolingual data to improve statistical machine translation (SMT) when only a small parallel training corpus is available. This strategy uses word classes derived from monolingual text data to improve the word alignment quality, which generally deteriorates significantly because of insufficient training. We pres...

 SUST, ICERIE. Abstract: — In this paper, we describe a research method that generates Bangla word clusters on the basis of relating to meaning in language and contextual similarity. The importance of word clustering is in parts of speech (POS) tagging, word sense disambiguation, text classification, recommender system, spell checker, grammar checker, knowledge discover and for many others Nat...

Search Pointers organize the main part of the application on the Internet. However, because of Information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. So the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. Community (web communit...

Many techniques have been used for document clustering that depended on the number of word occurrences in documents. In these techniques, words are considered as dimensions of the clustering space. Since a huge number of words is found in each document, studies were held to reduce this high dimensionality for better performance i.e., words pruning. Sampling was used to choose random documents r...

We focus on answering word analogy questions by using clustering techniques. The increased performance in answering word similarity questions can have many possible applications, including question answering and information retrieval. We present an analysis of clustering algorithms’ performance on answering word similarity questions. This paper’s contributions can be summarized as: (i) casting ...

We enhance the accuracy of the currently available semantic hashtag clustering method, which leverages hashtag semantics extracted from dictionaries such as Wordnet and Wikipedia. While immune to the uncontrolled and often sparse usage of hashtags, the current method distinguishes hashtag semantics only at the word-level. Unfortunately, a word can have multiple senses representing the exact sem...