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Extracting answer in Qa system: Learning based

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The method involves querying a search engine for web passages that contain the answer to the question, extracting patterns that characterize fine-grained classification for answers. Independent evaluation on a set of questions shows that the proposed approach outperforms a naive keyword based approach. | ISSN:2249-5789 Megha Mishra et al , International Journal of Computer Science & Communication Networks,Vol 2(3), 436-440 Extracting Answer in QA System: Learning Based Megha Mishra1, Vishnu Kumar Mishra2, H.R. Sharma3 1 Research Scholar SOA University 2 Asstt. Professor, BIT Durg 3 Dean R&D, RECT Raipur 1 megha16shukla@gmail.com vshn _mshr@rediffmail.com 3 hrsharmaji@indiatimes.com 2 to handling question analysis for QA systems. In our approach, training-data questions are first analyzed and classified into a set of fine-grained categories of question patterns. Then, the relationships between the question patterns and n-grams in answer passages are discovered by employing a word alignment technique. Finally, the best query transforms are derived by ranking the n-grams which are associated with a specific question pattern. At runtime, the keywords in a given question are extracted and the question is categorized. Then the keywords are expanded according the category of the question. The expanded query is the submitted to a search engine in order to bias the search engine to return passages that are more likely to contain answers to the question. Experimental results indicate the expanded query indeed outperforms the approach of directly using the keywords in the question. Abstract— Converting questions to effective queries is crucial to open-domain question answering systems. In this paper, we present a web-based unsupervised learning approach for transforming a given natural-language question to an effective query. The method involves querying a search engine for web passages that contain the answer to the question, extracting patterns that characterize fine-grained classification for answers. Independent evaluation on a set of questions shows that the proposed approach outperforms a naive keyword based approach. Keywords— Question Answering, Machine Learning, Query Retrieval. I. INTRODUCTION An automated question answering (QA) system receives a user‘s .