Hybrid CNN-LSTM Model for Answer Identification
Kavita Moholkar1, Suhas Patil2

1Kavita Moholkar, Bachelor of Computer Science and Engineering, Sant Gadge Baba Amravati University, Amravati (Maharashtra), India.
2Dr. S. H. Patil, Professor, Bharti Vidyapeeth College of Engineering, Pune (Maharashtra), India.

Manuscript received on 11 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 1163-1166 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4281098319/19©BEIESP | DOI: 10.35940/ijrte.C4281.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .
Keywords: Deep Neural Network, LSTM, Question Answer System, Recurrent Neural Network

Scope of the Article: Deep Learning