International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 25 Question Answering System using Artificial Neural Network Saifali Prasla 1 , Sairaj Tawde 2 , Vatsal Shah 3 , Jyoti Chavhan 4 1,2,3 B.E. student, Dept. of Information and Technology, Atharva College of Engineering, Maharashtra, India 4 Assistant Professor, Dept. of Information and Technology, Atharva College of Engineering, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - In today’s modern world, the Question Answering system is being used everywhere and it is a vital part of everyone’s lives as it allows people to search their queries and gain knowledge. However, the major issue with all the existing systems is that they all fail to answer complex questions which require interpretation of the data and question asked by the user. They are all limited to answering only simple and objective questions. The proposed system aims to get overcome this problem by creating a deep artificial neural network with associative memory from various documents like pdf, txt, etc. provided by the user. It processes the question asked by the user and comprehends the question, and understand its contextual meaning. After that, it then proceeds to find the answer from the deep neural network created from the documents. Key Words: Question Answering system, Artificial neural network, Natural Language Processing, Intelligent data retrieval system. 1. INTRODUCTION NLP is used to understand the knowledge provided to the system and to process that knowledge. A Question Answering System is used for fast extraction by exempting the user to read all the unnecessary information which might not lead to the answer. There are multiple NLP models used in the Question Answering System. These models are usually symbol matching which makes use of linguistic annotations, structured world knowledge, and semantic parsing. The neural network’s information processing mechanism is similar to the human brain. Due to this, it is used in various artificial intelligence models like pattern recognition, associative memory, etc. and they yield very high performance. Unlike any ML models, it doesn’t require training sets to develop models. The proposed system, that creates a deep neural network from the documents provided by the user and storing them for future use. It will try to imitate the human information recalling feature by processing the document first - understanding it and then try to find the answer to the questions asked. The system will process, comprehend and try to understand what is the answer to the question asked and then try to find the answer from the deep neural network created from the documents previously provided. 2. RELATED WORKS A Question Answering System is a system that reduces the human effort of searching for an answer by extracting it from its database or from the internet. Lu Liu and Jing Luo [1] had proposed a question answering system based on deep learning. Here the system used the CNN model and Word2Vec model to find the correct answer to the question asked. The reason for using these models is that the system was made to work in the Chinese language and in Chinese, there are no spaces between 2 words so eventually, the entire sentence is one single word and also the Chinese language has a lot of ambiguity in it. Sudha Morwal, Nusrat Jahan and Deepti Chopra [2] had proposed Name Entity Recognition using Hidden Markov Model (HMM). The HMM model was used to develop a language- independent NER. Yashvardhan Sharma and Sahil Gupta [3] had proposed a deep learning approach for question answering system, where the system used a basic AIML chat-bot to answer factual questions and then LSTM models and different memory networks to get accuracy as high as 98 percent. 3. PROPOSED SYSTEM AND IMPLEMENTATION 3.1 Proposed System The proposed QAS system comprises two phases namely the learning phase and the extraction phase. The first phase takes a document as an input, processes it and generates or updates the system’s neural network. After the document has been processed, the user can view the document and ask questions. In the second phase, the user can ask questions directly to the system. In this phase, the system takes a question as an input. The system then produces an answer to this question, if it has the answer, and displays the answer. Figure -1: Overview of the system. The block diagram in Figure 2 shows in schematic form the general arrangement of the parts or components of the proposed system.