Question Classification Based on Hybrid Neural Networks

Question classification is an important step in question answering system. There are many previous work based on machine learning about question classification. Although they are effective and practical, most of them require finding specific features to train the designed classifier. So it cannot be in common use in all the situations. Recently, deep learning methods has shown its remarkable strength in natural language processing. Deep neural networks like CNN and LSTM are helpful for sentence classification. The CNN is able to extract high-level local features while the LSTM can remember and discard information according to the context. Thus we implement a Hybrid Neural Network to handle different parts of a query. Both CNN and LSTM are utilized to achieve question classification. We conduct experiments comparing with methods based on machine learning and deep learning. The experimental results show the effectiveness and efficiency of our methods. Introduction The Question Answering (QA) system aims at automatically finding the answers to an arbitrary question in natural language. There are many successful QA systems such as IBM’s Wason , WolframAlpha and so on. The QA problem consists of several sub-problems, in which the question classification is included and it is important for question answering. The task of question classification is to predict the type of answer according to the query. In general, the question is described with title and description. As the questions in the question answering communities, such as Quora [3] and Yahoo Answers , the title of the question is typically short and straightforward while the description is usually longer and contains a lot of background information. Traditional question classification methods mostly are based on machine learning approach. For example, Naive Bayes, k-Nearest Neighbors and SVM algorithm can be used to implement the question classification. While the performance is dependent on the features used in the model. In most cases, bag-of-Words and bag-of-n-grams are the features utilized in machine learning approach. With the development of deep learning, neural network methods show the notable strength in Natural Language Processing (NLP). Though deep learning methods are firstly used in the field of computer vision, recent literature demonstrate its ability in text classification. The convolutional neural networks are capable of extracting the high-level features from local text by window filters. And the Long-Short Term Memory (LSTM) network is a special kind of Recurrent neural network (RNN), which is able to remember and ignore the features according to the context. Those characteristics are useful in question classification. In this paper, we combine those two kinds of neural networks into our framework to process different part of a question. And a Hybrid Neural Network (HNN) is implemented to achieve question classification. Related Work The Question Answering (QA) is a research activity which is related to Information Retrieval (IR) and NLP, but it is quite different from them in certain way. QA is a very practical activity and is 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) Copyright © 2016, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 50