Towards Automatic Topical Question Generation

Proceedings of COLING 2012: Technical Papers, pages 475–492, COLING 2012, Mumbai, December 2012. Towards Automatic Topical Question Generation Y llias Chali Sadid A. Hasan University of Lethbridge, Lethbridge, AB, Canada chali@cs.uleth.ca, hasan@cs.uleth.ca ABSTRACT We address the challenge of automatically generating questions from topics. We consider that each topic is associated with a body of texts containing useful information about the topic. Questions are generated by exploiting the named entity information and the predicate argument structures of the sentences present in the body of texts. To measure the importance of the generated questions, we use Latent Dirichlet Allocation (LDA) to identify the sub-topics (which are closely related to the original topic) in the given body of texts and apply the Extended String Subsequence Kernel (ESSK) to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for computing the syntactic correctness of the questions. The questions are ranked by considering their importance (in the context of the given body of texts) and syntactic correctness. To the best of our knowledge, no other study has accomplished this task in our setting before. Experiments show that our approach can significantly outperform the state-of-the-art results. KEYWORDS: Question generation, named entity information, predicate argument structures, latent dirichlet allocation (LDA), extended string subsequence kernel (ESSK), syntactic tree kernel. 475 1 Introduction When a user is served with a ranked list of relevant documents by the standard document retrieval systems (i.e. search engines), his/her search task is usually not over (Chali et al., 2009b). The next step for him/her is to look into the documents themselves and search for the precise piece of information he/she was looking for. This method is time consuming, and a correct answer could easily be missed, by either an incorrect query resulting in missing documents or by careless reading. This is why, Question Answering (QA) has received immense attention from the information retrieval, information extraction, machine learning, and natural language processing communities (Kotov and Zhai, 2010). One of the main requirements of a QA system is that it must receive a well-formed question as input in order to come up with the best possible correct answer as output. Available studies revealed that humans are not very skilled in asking good questions about a topic of their interest. They are forgetful in nature which often restricts them to properly express whatever that is peeking in their mind. Therefore, they would benefit from automated Question Generation (QG) systems that can assist in meeting their inquiry needs (Olney et al., 2012; Ali et al., 2010; Kotov and Zhai, 2010; Rus and Graesser, 2009; Lauer et al., 1992; Graesser et al., 2001). Question asking and Question Generation are important components in advanced learning technologies such as intelligent tutoring systems, and inquiry-based environments (Graesser et al., 2001). A QG system would be useful for building better question asking facilities in intelligent tutoring systems. Another benefit of QG is that it can be a good tool to help improve the quality of the …

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