The Implementation of Question Answer System Using Deep Learning

Vaishali Fulmal, Et Al

doi:10.17762/turcomat.v12i1s.1604

Abstract

Question-answer systems are referred to as advanced systems that can be used to provide answers to the questions which are asked by the user. The typical problem in natural language processing is automatic question-answering. The question-answering is aiming at designing systems that can automatically answer a question, in the same way as a human can find answers to questions. Community question answering (CQA) services are becoming popular over the past few years. It allows the members of the community to post as well as answer the questions. It helps users to get information from a comprehensive set of questions that are well answered. In the proposed system, a deep learning-based model is used for the automatic answering of the user’s questions. First, the questions from the dataset are embedded. The deep neural network is trained to find the similarity between questions. The best answer for each question is found as the one with the highest similarity score. The purpose of the proposed system is to design a model that helps to get the answer of a question automatically. The proposed system uses a hierarchical clustering algorithm for clustering the questions.

Highlights

In the area of natural language processing, one of the important challenge is to find if the two sentences convey the same meaning
The question similarity technique can be used for question answering (QA) system [1]
When a user asks a question, the QA system looks for possible similar questions in the available questions

Summary

Introduction

In the area of natural language processing, one of the important challenge is to find if the two sentences convey the same meaning. The system identifies the most similar questions using a deep learning algorithm. Answers of the questions which are identified in the previous steps are the correct answer of the asked question. In the question-answer system the important task is to determine the similarity between the questions pair. Answer selection is the task of giving an answer to the existing question which is most similar to the user’s question. The use of machine learning algorithms has increased because these algorithms are capable to solve many difficult tasks in different areas like science and engineering. Deep learning algorithms can be very efficient in automation of complex tasks. Deep learning methods produce a good performance which is not relying on any feature engineering or expensive external resources. The system is designed with Bi-LSTM and LSTM neural networks and the performance of both the algorithms are compared

Literature Survey

Input gate layer

Result and Discussion

Conclusion