Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Ambiguity measure feature-selection algorithm
Journal of the American Society for Information Science and Technology
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Detecting relationships among categories using text classification
Journal of the American Society for Information Science and Technology
Joint question clustering and relevance prediction for open domain non-factoid question answering
Proceedings of the 23rd international conference on World wide web
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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%).