@techreport{TD:100335,
	att_abstract={{This paper proposes a new topic inference framework that is built on the scalability and adaptability of mutual information (MI) techniques. The framework is designed to explore a more robust language model (LM) for general topic-oriented search terms in the domain of electronic programming guide (EPG) for broadcast TV programs. �The topic inference system selects the most relevant topics 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 web-based IR system. The MI-base topic induction system is able to achieve 98 percent accuracy in recall measurement and 82 percent accuracy in precision measurement on the test set.}},
	att_authors={hc4395},
	att_categories={C_IIS.11},
	att_copyright={{Springer-Verlag}},
	att_copyright_notice={{The definitive version was published in 16th International Conference on Applications of Natural Language to Information Systems  (Springer, LNCS). {{, Volume LNCS 6716}}{{, 2011-06-27}}}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Latent Semantic Analysis,  Natural Language Processing,  Mutual Information,  TF-IDF,  EPG},
	att_techdoc={true},
	att_techdoc_key={TD:100335},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100335_DS1_2011-06-21T14:22:12.309Z.pdf},
	author={Hisao Chang},
	institution={{16th International Conference on Applications of Natural Language to Information Systems}},
	month={June},
	title={{Topics Inference by Weighted Mutual Information Measures Computed from Structured Corpus}},
	year=2011,
}