标题：Improved Unsupervised Framework for Solving Synonym, Homonym, Hyponymy & Polysemy Problems from Extracted Keywords and Identify Topics in Meeting Transcripts
期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2012
卷号：2
期号：5
DOI：10.5121/ijcsea.2012.2508
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
