Unsupervised Chinese Verb Metaphor Recognition Based on Selectional Preferences
نویسندگان
چکیده
Metaphors are pervasive in human language and developing methods to recognize and deal with metaphors is an indispensable task in Natural Language Processing (NLP). This paper proposes an unsupervised method to recognize metaphors from real texts. Firstly, source domain candidates are determined based on automatically acquired selectional preferences. And then metaphors are recognized with the source domain knowledge. Experiment results show that this unsupervised method outperforms the baseline by a great improvement. In addition, the source domain knowledge can also be used for metaphor comprehension.
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