Answer Validation in Question-Answering System using Support Vector Machine

Question-answering system is a special type of information retrieval system. Considering a set of documents (such as Web or local set of documents), the system should answer the questions asked in the natural language form. Question-answering systems generally consist of question processing and analysis, keyword generation, information retrieval, answer extraction, and answer validation. In such systems, selecting proper answers for user question is an important task which influences performance of the whole system. An appropriate answer validation technique can increase the performance of the question-answering system. In this study, Support Vector Regression (SVR) is employed in order to perform answer validation. SVR eliminates the risk of getting stuck in local minima by reducing the operational risk. The proposed system is applied on some of the TREC and Wikipedia questions repositories. In order to evaluate performance of the proposed system, following measures are calculated: Mean Reciprocal Ranking (MRR) and F-measure. Based on experimental results, the proposed system achieved 81% MRR and 49.7% F-measure which shows higher performance than systems with no answer validation and also systems using neural network-based answer validation.