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Information Retrieval using Markov random Fields and Restricted Boltzmann Machines

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posted on 06.04.2021, 14:23 by Monika Kamma
When a user types in a search query in an Information Retrieval system, a list of top ‘n’ ranked documents relevant to the query are returned by the system. Relevant means not just returning documents that belong to the same category as that of the search query, but also returning documents that provide a concise answer to the search query. Determining the relevance of the documents is a significant challenge as the classic indexing techniques that use term/word frequencies do not consider the term (word) dependencies or the impact of previous terms on the current words or the meaning of the words in the document. There is a need to model the dependencies of the terms in the text data and learn the underlying statistical patterns to find the similarity between the user query and the documents to determine the relevancy.

This research proposes a solution based on Markov Random Fields (MRF) and Restricted Boltzmann Machines (RBM) to solve the problem of term dependencies and learn the underlying patterns to return documents that are very similar to the user query.

History

Degree Type

Master of Science

Department

Computer and Information Technology

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Thomas J Hacker

Additional Committee Member 2

Dawn Laux

Additional Committee Member 3

Abdul Salam

Additional Committee Member 4

Dominic Kao

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