Ranking of Document Recommendations from Conversations using Probabilistic Latent Semantic Analysis
Journal Title: GRD Journal for Engineering - Year 2016, Vol 1, Issue 0
Abstract
Any Information retrieval from documents is done through text search. Now a day, efficient search is done through Mining techniques. Speech is recognized for searching a document. A group of Conversations are recorded using Automatic Speech Recognition (ASR) technique. The system changes speech to text using FISHER tool. Those conversations are stored in a database. Formulation of Implicit Queries is preceded in two stages as Extraction and Clustering. The domain of the conversations is structured through Topic Modeling. Extraction of Keywords from a topic is done with high probability. In this system, Ranking of documents is done using Probabilistic Latent Semantic Analysis (PLSA) technique. Clustering of keywords from a set covers all the topics recommended. The precise document recommendation for a topic is specified intensively. The Probabilistic Latent Semantic Analysis (PLSA) technique is to provide ranking over the searched documents with weighted keywords. This reduces noise while searching a topic. Enforcing both relevance and diversity ensures effective document retrieval. The text documents are converted to speech conversation using e-Speak tool. The final retrieved conversations are as required.
Authors and Affiliations
P. Velvizhi, S. Aishwarya, R. Bhuvaneswari
Keywords
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