Lin M.-S.HSIN-HSI CHEN2019-07-102019-07-1020099780769538013https://scholars.lib.ntu.edu.tw/handle/123456789/413168This paper defines a novel relatedness measure by conditional query, explores snippets in various web domains as corpora, and evaluates the relatedness measure on three famous benchmarks, including WordSimilarity-353, Miller- Charles and Rubenstein-Goodenough datasets. Conditional query Q Y|X on a web domain estimates frequency f Y|X by querying Y to search engine results of X. Dependency score is in terms of frequencies f Y|X and f X|Y, and content overlap of search results of X and Y by various operations. A transfer function projects dependency score to mutual dependency of X and Y. Two transfer functions based on Poisson and Gompertz models are considered. Gompertz model reports the correlation score 0.706 in the WordSimilarity-353 dataset. Gompertz model also shows the best performance among all the web-based approaches in Rubenstein-Goodenough and Miller-Charles datasets. ? 2009 IEEE.Community chain detectionQuery suggestionRelatedness measure[SDGs]SDG17conference paper10.1109/WI-IAT.2009.862-s2.0-84863128477https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863128477&doi=10.1109%2fWI-IAT.2009.86&partnerID=40&md5=0d3198a593fd4eaf7a08e857142207c7