Improved Unsupervised Framework for Solving Synonym, Homonym, Hyponymy & Polysemy Problems from Extracted Keywords and Identify Topics in Meeting Transcripts

Abstract

Keyword is the important item in a document that provides efficient access to the content of a document. In the Existing system, Synonym, Homonym, Hyponymy and Polysemy problems were solved from only trained extracted keywords in the meeting transcripts. Synonym problem means different words which have similar meaning they are grouped and single keyword is extracted. Hyponymy problem means one word denoting subclass that is considered and super class keyword is extracted. Homonym means a word which can have two or more different meanings.. A Polysemy means word with different, but related senses. Hidden topics from meeting transcripts can be found using LDA model. MaxEnt classifier is used for extracting keywords and topics which will be used for information retrieval Training the keyword from the dataset is separately needed for all the problems, it is not an automatic one .In this proposed frame work, a dataset has been designed to solve the above mentioned four problems automatically.

Data Pre-processing in Text Mining

“…In order to carry out studies such as classification and clustering on text data, it is necessary to determine the attributes of the data elements, and even if the data is a single paragraph text, almost every word will come as a separate attribute, and processing these attributes causes a great deal of time and space consumption. The ambiguity of the language can be examined in four categories (Sheeba & Vivekanandan, 2012):…”