Main Article Content
This paper explores the idea of keyword extraction from conversations, the goal of using these keywords to retrieve, for each short
conversation text file, a small number of possibly relevant documents, which can be recommended to the participants. However, even a short
conversation contains different types of words, which are absolutely related to several topics; Therefore, it is difficult to infer precisely the
information needs of the conversation participants. The existing system proposed a diverse keyword extraction technique which extracts the
keyword from the meeting conversation transcripts and recommends the document to the participants. So, in this paper we first propose an
algorithm to extract keywords from the output of preprocessing process where string is processed to its basic meaning by following the basic
four activities. Then, we propose a feature extraction method to extract multiple topically differentiated queries from this keyword set, in order to
maximize the chances of making at least one relevant recommendation to participants. The proposed methods are evaluated in terms of relevance
with respect to conversation fragments from the conversation text file. The results shows that our system improves over previous methods that
consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in
Keywords: Document recommendation, keyword extraction, feature extraction, fuzzy logic and weight matrix method.
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