Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using mutual information to resolve query translation ambiguities and query term weighting
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Introduction to Information Retrieval
Introduction to Information Retrieval
Parsing a natural language using mutual information statistics
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Conceptual modeling of online entertainment programming guide for natural language interface
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Enriching domain-specific language models using domain independent WWW n-gram corpus
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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This paper proposes a novel topic inference framework that is built on the scalability and adaptability of mutual information (MI) techniques. The framework is designed to systematically construct a more robust language model (LM) for topic-oriented search terms in the domain of electronic programming guide (EPG) for broadcast TV programs. The topic inference system identifies the most relevant topics implied from a search term, based on a simplified MI-based classifier trained from a highly structured XML-based text corpus, which is derived from continuously updated EPG data feeds. The proposed framework is evaluated against a set of EPG-specific queries from a large user population collected from a real world web-based IR system. The MI-base topic inference system is able to achieve 98 percent accuracy in recall measurement and 82 percent accuracy in precision measurement on the test set.