Question terminology is a set of terms which appear in keywords, idioms and fixed expressions commonly observed in questions. This paper investigates ways to automatically extract question terminology from a corpus of questions and represent them for the purpose of classifying by question type. Our key interest is to see whether or not semantic features can enhance the representation of strongly lexical nature of question sentences. We compare two feature sets: one with lexical features only, and another with a mixture of lexical and semantic features. For evaluation, we measure the classification accuracy made by two machine learning algorithms, C5.0 and PEBLS, by using a procedure called domain cross-validation, which effectively measures the domain transferability of features.
Mansouri, Behrooz, Richard Zanibbi & Douglas W. Oard
2019. 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), ► pp. 57 ff.
Yan, Xia
2013. Question Understanding and Similarity Computation Method Based on Semantic Analysis. In Proceedings of the 2012 International Conference on Information Technology and Software Engineering [Lecture Notes in Electrical Engineering, 211], ► pp. 699 ff.
Hao, Tianyong & Eugene Agichtein
2012. Finding similar questions in collaborative question answering archives: toward bootstrapping-based equivalent pattern learning. Information Retrieval 15:3-4 ► pp. 332 ff.
Hao, Tianyong & Eugene Agichtein
2012. Bootstrap-Based Equivalent Pattern Learning for Collaborative Question Answering. In Computational Linguistics and Intelligent Text Processing [Lecture Notes in Computer Science, 7182], ► pp. 318 ff.
Xin Kang, Xiaojie Wang & Fuji Ren
2008. 2008 International Conference on Natural Language Processing and Knowledge Engineering, ► pp. 1 ff.
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