Evaluation of question classification systems using differing features

Abstract

Most question and answer systems"Q&A" are based on three research themes: question classification and analysis, document retrieval and answer extraction. The performance in every stage affects the final result. The classification of questions appears as an important task because it deduces the type of expected answers. A method of improving the performance of question classification is presented, based on linguistic analysis (semantic, syntactic and morphological) as well as statistical approaches guided by a layered semantic hierarchy of fine grained question types. Actually, methods of question expansion are studied. This method adds for each word a higher representation. Various features of questions, diverse term weightings and several machine learning algorithms are compared. Experiments were conducted on real data are presented. They demonstrate an improvement in precision for question classification.

Enhanced semantic expansion for question classification

“…In the work of Fu et al (2009), they used a classifier based on SVM combined with a question semantic similarity. Harb et al (2009) describe a comparison between the performances of different classifiers. They extract discriminant words from question.…”

“…Traditionally, many QA systems use manually constructed rules (Kosseim and Yousefi, 2007;Plamondon et al, 2003;Kangavari et al, 2008;Saxena et al, 2007) to typify the questions, which is not very efficient for maintenance and upgrading. Recently, with the growing popularity of statistical approaches, machine learning was applied to detect the categories of questions (Harb et al, 2009;Krishnan et al, 2005;Hacioglu and Ward, 2003;Zhang and Lee, 2003;Li and Roth, 2006;Fu et al, 2009). The advantage is that machine learning algorithms can recognise among discriminating features, and rely on the learning process to efficiently cope with the features.…”

A Question Answering System on Holy Quran Translation Based on Question Expansion Technique and Neural Network Classification

“…There are two important factors to ensure the success of QAS; (1) analyze the users' needs (queries) efficiently using Natural Processing Language (NLP) and (2) classify and manage the documents that contain the candidates answers accurately based on document classification phase. Therefore, the accurate matching between users' questions and the proposed answers will be found effectively (Harb et al, 2009;Tan et al, 2009).…”