Master of Science in Computer Science
First Committee Member
Kazem Taghva, Chair
Second Committee Member
Ajoy K. Datta
Third Committee Member
Laxmi P. Gewali
Graduate Faculty Representative
Number of Pages
There is a continuous progress in automatic recording of broadcast speech using speech recognition. With the increasing use of this technology, a new source of data is added to the pool of information available over web. This has increased the need to categorize the resulting text, based on their topic for the purpose of information retrieval.
In this thesis we present an approach to automatically assign a topic or track a change of topic in a stream of input data. Our approach is based on the use of Hidden Markov Models and language processing techniques. We consider input text as stream of words and use Hidden Markov Model to assign the most appropriate topic to the text. Then we process this output to identify the topic boundaries. The main focus of this thesis is to automatically assign a topic to specific story.
Automatic indexing; Automatic speech recognition; Hidden Markov models; Information retrieval; Sound recordings
Computer Sciences | Theory and Algorithms
Tatavarty, Aditya S., "Topic detection and tracking using hidden Markov models" (2011). UNLV Theses, Dissertations, Professional Papers, and Capstones. 907.