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CIKM
2009
Springer

Semi-supervised learning of semantic classes for query understanding: from the web and for the web

9 years 3 months ago
Semi-supervised learning of semantic classes for query understanding: from the web and for the web
Understanding intents from search queries can improve a user’s search experience and boost a site’s advertising profits. Query tagging via statistical sequential labeling models has been shown to perform well, but annotating the training set for supervised learning requires substantial human effort. Domain-specific knowledge, such as semantic class lexicons, reduces the amount of needed manual annotations, but much human effort is still required to maintain these as search topics evolve over time. This paper investigates semi-supervised learning algorithms that leverage structured data (HTML lists) from the Web to automatically generate semantic-class lexicons, which are used to improve query tagging performance – even with far less training data. We focus our study on understanding the correct objectives for the semi-supervised lexicon learning algorithms that are crucial for the success of query tagging. Prior work on lexicon acquisition has largely focused on the precision...
Ye-Yi Wang, Raphael Hoffmann, Xiao Li, Jakub Szyma
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Ye-Yi Wang, Raphael Hoffmann, Xiao Li, Jakub Szymanski
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