Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System

TL;DR

This paper explores the use of convolutional neural networks for text classification in open domain question answering systems, demonstrating the feasibility of large-scale training and integration of CNN classifiers.

Contribution

It introduces a CNN-based multi-class text classifier trained on large datasets and proposes a method to integrate it into open domain question answering systems.

Findings

CNN classifier trained on two datasets shows promising results.

The model effectively maps semantically related words using word embeddings.

Integration method enhances open domain question answering capabilities.

Abstract

Recently machine learning is being applied to almost every data domain one of which is Question Answering Systems (QAS). A typical Question Answering System is fairly an information retrieval system, which matches documents or text and retrieve the most accurate one. The idea of open domain question answering system put forth, involves convolutional neural network text classifiers. The Classification model presented in this paper is multi-class text classifier. The neural network classifier can be trained on large dataset. We report series of experiments conducted on Convolution Neural Network (CNN) by training it on two different datasets. Neural network model is trained on top of word embedding. Softmax layer is applied to calculate loss and mapping of semantically related words. Gathered results can help justify the fact that proposed hypothetical QAS is feasible. We further propose a method to integrate Convolutional Neural Network Classifier to an open domain question answering system. The idea of Open domain will be further explained, but the generality of it indicates to the system of domain specific trainable models, thus making it an open domain.