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The motivation of our approach is that the general method can be used with different text representations and in combination of other TC techniques. Experiments on several test collections show that our context-dependent feature values can improve TC over traditional context-independent unigram feature values, using a strong classifier like Support Vector Machine (SVM), which past works have found to be hard to improve. We also show that the relative performance improvement of our method over the context-independent baseline is comparable to the levels attained by recent word embedding methods in the literature, while an advantage of our approach is that it does not require the substantial training needed to learn word embedding representations.<\/jats:p>","DOI":"10.1142\/s021819402050031x","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T09:53:03Z","timestamp":1603273983000},"page":"1199-1219","source":"Crossref","is-referenced-by-count":3,"title":["Context-Dependent Feature Values in Text Categorization"],"prefix":"10.1142","volume":"30","author":[{"given":"Edward Kai Fung","family":"Dang","sequence":"first","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert Wing Pong","family":"Luk","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Allan","sequence":"additional","affiliation":[{"name":"College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003-9264, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"S021819402050031XBIB001","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1016\/j.jbi.2012.04.010","volume":"45","author":"Garla V. 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