Question classification based on an extended class sequential rule model

Proceedings of the 5th International Joint Conference on Natural Language Processing, pages 938–946, Chiang Mai, Thailand, November 8 – 13, 2011. c⃝2011 AFNLP Question Classification Based on an Extended Class Sequential Rule Model Zijing Hui, Juan Liu*, Lumei Ouyang Computer School, Wuhan University, Wuhan 40072, P. R. China {zijinghui, liujuan, ouyanglumei}@whu.edu.cn Abstract Question classification is a crucial preprocessing for question answering system; it can help to make sure the user’s intention. Most of previous researches fo- cus on the feature driven methods that represent a question with a bag of features, which ignore the im- portant information contained in the words order and distance. To take such information into account, this paper proposes to describe the questions via the ExCSR (Extended Class Sequential Rule) model. To mine ExCSR rules, a method based on PrefixSpan, called DS-SRM (Distance Sensitive Sequential Rule Miner), is presented as well. Due to the existence of redundancy in the mined rules, a rule selection algo- rithm MCRSelection (Most Cover Rule Selection) is also proposed to find the most interesting rules. Ex- periments results on UIUC question set1 show that the proposed method can achieve the accuracy of 90.6%, which outperforms the previously reported results. 1 Introduction Question classification, i.e., classifying ques- tions into predetermined types, is quite an im- portant problem in question answering (QA) sys- tem, it helps to make clear the intention of users so that the system can choose the appropriate strategies of answers searching and ranking. Similar to other classification problems, question classification usually needs to build the classifier from the training data which contains a set of labeled questions; the classifier is then used to classify the unlabeled questions. Although questions are special kinds of texts, question classification is more challenging than text classification. Compared to normal texts, a question is usually a very short sentence (some even with few of words), and mostly one word just occurs once in one question, which results that the widely used vector space model (mainly based on TF/IDF) fails to work. To address this problem, researchers usually develop a lot of fea- * the corresponding author 1 http://cogcomp.cs.illinois.edu/Data/QA/QC/ tures, such as location, organization, name, etc., to annotate the words/phrases in the questions, and then use the bag of features to represent the questions. However, such methods still suffer some problems. (1) To get the satisfactory classi- fication performances, they usually need an ex- tremely large amount of features. For example, Li et al. used more than 200,000 features (Li et al., 2006) to represent questions in UIUC ques- tion set, while Huang et al. used 13,697 binary features in their best feature space (Huang et al., 2008). (2) The bag of features representation ig- nores the relationship and the order information among words within the questions, which will cause the misclassification. For example, “Which city is famous for rose?” and “Which rose is fa- mous for city?” belong to two different classes (the former one asks about the <location>, while the later one is …

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