A SURVEY ON QUESTION AND ANSWER SYSTEM BY RETRIEVING THE DESCRIPTIONS USING LOCAL MINING AND GLOBAL LEARNING (original) (raw)

Question answering is a modern type of data recovery described by data needs that are at any rate somewhat communicated as normal dialect articulations or addresses, and is a standout amongst the most regular types of human PC cooperation. This article gives an extensive and relative review of Question Answering Technology (QAT). Question retrieval in current community-based question answering (CQA) administrations does not, all in all, function admirably for long and complex inquiries. This paper introduces the quality question and answer (QA) sets amassed as thorough information bases of human knowledge. It helps clients to look for exact data by acquiring right answers straightforwardly, as opposed to skimming through substantial ranked arrangements of results. Hence to retrieve relevant questions and their corresponding answers becomes an important task for information acquisition. This paper discusses different focus of the QA task which is transformed from answer extraction, answer matching and answer ranking to searching for relevant questions with good ready answers.

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