Abstract
Automatic identification of subtopics for a given topic is desirable because it eliminates the need for manual construction of domain-specific topic hierarchies. In this paper, we design features based on corpus statistics to design a classifier for identifying the (subtopic, topic) links between phrase pairs. We combine these features along with the commonly-used syntactic patterns to classify phrase pairs from datasets in Computer Science and WordNet. In addition, we show a novel application of our is-a-subtopic-of classifier for query expansion in Expert Search and compare it with pseudo-relevance feedback.
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Das, S., Mitra, P., Giles, C.L. (2012). Phrase Pair Classification for Identifying Subtopics. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_48
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DOI: https://doi.org/10.1007/978-3-642-28997-2_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28996-5
Online ISBN: 978-3-642-28997-2
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