Ranking of Document Recommendations from Conversations using Probabilistic Latent Semantic Analysis

P.Velvizhi, K.L.N. College of Engineering; S.Aishwarya ,; R.Bhuvaneswari ,

Keyword Extraction, Topic Modeling, Word Frequency, PLSA, Document retrieval

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.
Paper ID: GRDCF002031
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 133 - 138