Topic Classification Based on Improved Word Embedding

Abstract

Topic classification is a foundational task in many NLP applications. Traditional topic classifiers often rely on many humandesigned features, while word embedding and convolutional neural network based on deep learning are introduced to realize topic classification in recent years. In this paper, the influence of different word embedding for CNN classifiers is studied, and an improved word embedding named HybridWordVec is proposed, which is a combination of word2vec and topic distribution vector. Experiment on Chinese corpus Fudan set and English corpus 20Newsgroups is conducted. The experiment turns out that CNN with HybridWordVec gains an accuracy of 91.82% for Chinese corpus and 95.67% for English corpus, which suggests HybridWordVec can obviously improve the classification accuracy comparing with other word embedding models like word2vec and GloVe.