A Study on Paraphrase Recognition Using Radial Basis Function Neural Network

Abstract

Paraphrasing is the restatement of a given text using alternate words. Recognition of paraphrases is vital in applications such as question answering, information extraction, and multi-document summarization. Lexical, syntactic, and semantic features of text can be used either individually or in combinations for recognizing paraphrases. Several machine-learning classifiers such as support vector machines (SVM), nearest neighbour method, and decision trees have been used for paraphrase recognition with SVM recognizers being the most popular ones. This paper explores the applicability of neural networks for paraphrase recognition. A radial basis function neural network (RBFNN) has been designed and implemented for recognizing paraphrases. Experiments have been carried out on the Microsoft research paraphrase corpus. From the results of the experiments, it has been observed that the RBFNN recognizer consistently outperforms the SVM recognizer with respect to accuracy and that the best performance was achieved when a combination of lexical, syntactic, and semantic features were used.