Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

Shortage of manually sense-tagged data is an obstacle to supervised word sense
disambiguation methods. In this paper we investigate a label propagation based …

Text Summarization is the procedure by which the significant portions of a text are retrieved.
Most of the approaches perform the summarization based on some hand tagged rules, such …

Text document clustering plays an important role in providing better document retrieval,
document browsing, and text mining. Traditionally, clustering techniques do not consider the …

This paper presents a grammar and semantic corpus based similarity algorithm for natural
language sentences. Natural language, in opposition to “artificial language”, such as …

In the fast-evolving digital era, consumers increasingly rely on others' opinions about a
product or service rather than corporations' promotional material. This study analyzed online …

Abstract Classical or traditional Information Retrieval (IR) approaches rely on the word-
based representations of query and documents in the collection. The specification of the …

This chapter describes core tasks that are routinely performed on natural language texts.
Some of these tasks are often considered as preprocessing and form the foundation for …

Market surveillance systems (MSSs) are information systems that monitor financial markets
to combat market abuses. Existing MSSs focus mainly on analyzing trading activities and are …

An attempt is made in this paper to report how a supervised methodology has been adopted
for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications …