Nazli Goharian
ABSTRACT
The context of the user queries, preceding a given query, is utilized to improve the effectiveness of query classification. Earlier efforts utilize fixed number of preceding queries to derive such context information. We propose and evaluate an approach (DQW) that identifies a set of unambiguous preceding queries in a dynamically determined window to utilize in classifying an ambiguous query. Furthermore, utilizing a relationship-net (R-net) that represents relationships among known categories, we improve the classification effectiveness for those ambiguous queries whose predicted category in this relationship-net is related to the category of a query within the window. Our results indicate that the hybrid approach (DQW+R-net) statistically significantly improves the Conditional Random Field (CRF) query classification approach when static query windowing and hierarchical taxonomy are used (SQW+Tax), in terms of precision (10.8%), recall (13.2%), and F1 measure (11.9%).
- Cao, H., Hu, D. H., Shen, D., Jiang, D., Sun, J., Chen, E., and Yang, Q., Context-aware query classification. SIGIR, 2009 Google Scholar
Digital Library
- Lafferty, J. D., McCallum, A., and Pereira, F. C., Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML, 2001 Google ScholarDigital Library
- Mengle, S., Goharian, N., Ambiguity measure feature-selection algorithm. JASIST, 60(5), 2009 Google ScholarDigital Library
- Mengle, S., Goharian, N., Detecting relationships among categories using text classification. JASIST, 61(5), 2010 Google ScholarDigital Library
Index Terms
- Context aware query classification using dynamic query window and relationship net
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