Temporal Intent Classification with Query Expression Feature
Sisi Gui1,2,Wei Lu3,Xiaojuan Zhang4()
1School of Information Management, Wuhan University, Wuhan 430072, China 2Institute for Information Retrieval and Knowledge Mining, Wuhan University, Wuhan 430072, China 3Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China 4School of Computer and Information Science, Southwest University, Chongqing 400715, China
[Objective] This paper investigates the effectiveness of query-based features and compares the performance of two types of classifiers in a query temporal intent classification task. [Methods] This paper first reviews all query-based features and then classifies those features into three types, according to their temporal relevance, namely, atemporal, implicit temporal and explicit temporal. Then, it tests accuracy of a temporal query intent classification task, using a supervised classifier and a semi-supervised classifier individually, with various combinations of query-based features of different types. [Results] Among all tested query-based features, using explicit temporal features achieves best accuracy, especially for the feature on whether a query contains a year; The performance hardly varies across classifiers; Our best macro average accuracy of 81.14% is higher than that in previous studies with the same experimental setups. [Limitations] Due to accessibility of dataset, our experiments are done on a limited size dataset. Only existing query-based features are studied and no new feature is proposed or tested. [Conclusions] Using highly temporal relevant features can improve accuracy in temporal query intent classification task, whereas using slightly temporal relevant features could hardly improve accuracy.
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