Paper Info
Title
Mining User Intentions from Medical Queries: A Neural Network Based Heterogeneous Jointly Modeling Approach.
Abstract
Text queries are naturally encoded with user intentions. An intention detection task tries to model and discover intentions that user encoded in text queries. Unlike conventional text classification tasks where the label of text is highly correlated with some topic-specific words, words from different topic categories tend to co-occur in medical related queries. Besides the existence of topic-specific words and word order, word correlations and the way words organized into sentence are crucial to intention detection tasks. In this paper, we present a neural network based jointly modeling approach to model and capture user intentions in medical related text queries. Regardless of the exact words in text queries, the proposed method incorporates two types of heterogeneous information: 1) pairwise word feature correlations and 2) part-of-speech tags of a sentence to jointly model user intentions. Variable-length text queries are first inherently taken care of by a fixed-size pairwise feature correlation matrix. Moreover, convolution and pooling operations are applied on feature correlations to fully exploit latent semantic structure within the query. Sentence rephrasing is finally introduced as a data augmentation technique to improve model generalization ability during model training. Experiment results on real world medical queries have shown that the proposed method is able to extract complete and precise user intentions from text queries.
Year
DOI
Venue
2016
10.1145/2872427.2874810
WWW
Keywords
Field
DocType
Text Query, Intention Detection, Neural Network, Jointly Modeling
Data mining,Computer science,Artificial intelligence,Artificial neural network,Pairwise comparison,World Wide Web,Feature correlation,Word order,Convolution,Pooling,Exploit,Sentence,Machine learning
Conference
Citations 
PageRank 
References 
8
0.50
26
Authors
4
Name
Order
Citations
PageRank
Chenwei Zhang17113.96
Wei Fan24205253.58
Nan Du350352.49
Philip S. Yu4306703474.16