Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification

Abstract

Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn representations of phrase/sentence, however, rich syntactic knowledge has not been fully explored when composing a longer text from its shorter constituent words. In most traditional models, only word embeddings are utilized to compose phrase/sentence representations, while the syntactic information of words is yet to be explored. In this article, we discover that encoding syntactic knowledge (part-of-speech tag) in neural networks can enhance sentence/phrase representation. Specifically, we propose to learn tag-specific composition functions and tag embeddings in recursive neural networks, and propose to utilize POS tags to control the gates of tree-structured LSTM networks. We evaluate these models on two benchmark datasets for sentiment classification, and demonstrate that improvements can be obtained with such syntactic knowledge encoded.