Information Retrieval Using Effective Bigram Topic Modeling

Improved topic-dependent language modeling using information retrieval techniques

N-gram language models are frequently used by the speech recognition systems to constrain and guide the search. N-gram models use only the last N-1 words to predict the next word. Typical values of N that are used range from 2-4. N-gram language models thus lack the long-term context information. We show that the predictive power of the N-gram language models can be improved by using long-term ...

Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request—searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can leads to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topica...

Bigram Anchor Words Topic Model

A probabilistic topic model is a modern statistical tool for document collection analysis that allows extracting a number of topics in the collection and describes each document as a discrete probability distribution over topics. Classical approaches to statistical topic modeling can be quite effective in various tasks, but the generated topics may be too similar to each other or poorly interpr...

Topic Models in Information Retrieval

Topic Modeling and Classification of Cyberspace Papers Using Text Mining

The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspac...