Convolutional recurrent neural network with template based representation for complex question answering
| Main Authors: | A. Chandra Obula Reddy, K. Madhavi |
|---|---|
| Format: | Article |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4105777 |
| ctrlnum |
4105777 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>A. Chandra Obula Reddy</creator><creator>K. Madhavi</creator><date>2020-06-01</date><description>Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.</description><identifier>https://zenodo.org/record/4105777</identifier><identifier>10.11591/ijece.v10i3.pp2710-2718</identifier><identifier>oai:zenodo.org:4105777</identifier><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>International Journal of Electrical and Computer Engineering (IJECE) 10(3) 2710-2718</source><subject>Complex question answering</subject><subject>Convolutional recurrent neural network</subject><subject>Natural language processing</subject><subject>Question answer pair</subject><subject>Template</subject><title>Convolutional recurrent neural network with template based representation for complex question answering</title><type>Journal:Article</type><type>Journal:Article</type><recordID>4105777</recordID></dc>
|
| format |
Journal:Article Journal |
| author |
A. Chandra Obula Reddy K. Madhavi |
| title |
Convolutional recurrent neural network with template based representation for complex question answering |
| publishDate |
2020 |
| topic |
Complex question answering Convolutional recurrent neural network Natural language processing Question answer pair Template |
| url |
https://zenodo.org/record/4105777 |
| contents |
Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier. |
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