Open AccessHybrid CNN-LSTM Model for Answer Identification
Author(s) -
Kavita Moholkar,
Suhas H. Patil
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4281.098319
Subject(s) - computer science , relevance (law) , artificial intelligence , deep learning , semantics (computer science) , identification (biology) , context (archaeology) , exploit , task (project management) , natural language processing , key (lock) , feature (linguistics) , convolutional neural network , machine learning , information retrieval , paleontology , linguistics , philosophy , botany , computer security , management , political science , law , economics , biology , programming language
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 .