Study on the Chinese Word Semantic Relation Classification with Word Embedding

This paper describes our solution to the NLPCC 2017 shared task on Chinese word semantic relation classification. Our proposed method won second place for this task. The evaluation result of our method on the test set is 76.8% macro F1 on the four types of semantic relation classification, i.e., synonym, antonym, hyponym, and meronym. In our experiments, we try basic word embedding, linear regression and convolutional neural networks (CNNs) with the pre-trained word embedding. The experimental results show that CNNs have better performance than other methods. Also, we find that the proposed method can achieve competitive results with small training corpus.

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