A Prediction Model For Web Search Hit Counts Using Word Frequencies

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Using an ontology to improve the web search experience

strings and implements a model for predicting the number of search engine hits for each alternative query string, based on the English language frequencies of the words in the search terms. Evaluation experiments of the hit count prediction model are presented for three major search engines. The dissertation also discusses and quantifies how

Combining Language Sources and Robust Semantic Relatedness

Yahoo Web (HC). The web itself is apparently the largest collection of textual content. For semantic relatedness computation, actual content is usually summa-rized in the form of search engine (Yahoo) hit counts (HC). The Dice coe cient then measures similarity of two terms by the relative number of co-occurrences, inferred from hit counts sim

SOON AE CHUN - City University of New York

[13] Tian T., S. Chun and J. Geller (2011) Prediction Model for Web Search Hit Counts Using Word Frequencies, Journal of Information Sciences, Vol. 37(5): 462 - 475. [14] Chun S. and J. Warner (2010) Finding Information in an Era of Abundance: towards a