Abstract
Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (i) A deep neural network classifier, which produces sentence vector considering word correlation and context. (ii) Zero shot classifier that uses a multi-channel Convolutional Neural Network (CNN), to extract knowledge from multiple sources in the knowledge accumulator. This output acts as a knowledge enhancer that strengthens the passage level summary. (iii) Summary generator that uses Maximal Marginal Relevance (MMR) algorithm, which computes similarity among the query related answer and the sentences from zero shot classifier. This model is applied on the datasets WikiPassageQA and ANTIQUE. The experimental analysis shows that this model gives comparatively better results for WikiPassageQA dataset.
Highlights
Information retrieval (IR) is a wide area that covers the extraction of specific information from a pool of information resources
This paper proposes a framework for a non-factoid question answering (QA) system that has three main components: (1) a deep neural network classifier, which produces sentence vector considering word correlation and context; (2) zero shot classifier that uses a multi-channel convolutional neural network (CNN) to extract knowledge from multiple sources in the knowledge accumulator, which acts as a knowledge enhancer that strengthens the passage level summary; (3) summary generator that uses maximal marginal relevance (MMR) algorithm, which computes similarity among the query-related answers and the sentences from the zero shot classifier
When comparing the result of Bi-Gated Recurrent Units (GRU)+ CNN with Bidirectional GRU (Bi-GRU)+ CNN + Multichannel CNN, there is a good improvement in the values obtained
Summary
Information retrieval (IR) is a wide area that covers the extraction of specific information from a pool of information resources. Question Answering (QA) Systems could address the current user’s needs by returning passages as answers. The QA systems remains a boon to the teaching and learning community, as it provides short answers instead of long documents. Few works emphasized on customizing QA systems, to facilitate e-learning. In closed domain QA systems, exact answers for questions were obtained with the extensive use of Natural Language Processing (NLP) techniques.
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