Abstract
This paper describes the system we submitted to Task 1, i.e., Chinese Word Semantic Relation Classification, in NLPCC 2017. Given a pair of context-free Chinese words, this task is to predict the semantic relationships of them among four categories: Synonym, Antonym, Hyponym and Meronym. We design and investigate several surface features and embedding features containing word level and character level embeddings together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.
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Acknowledgements
This research is supported by grants from NSFC (61402175), Science and Technology Commission of Shanghai Municipality (14DZ2260800 and 15ZR1410700), Shanghai Collaborative Innovation Center of Trustworthy Software for Internet of Things (ZF1213) and Duty Collection Center (Shanghai) of the General Administration of Customs.
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Zhou, Y., Lan, M., Wu, Y. (2018). Effective Semantic Relationship Classification of Context-Free Chinese Words with Simple Surface and Embedding Features. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_38
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DOI: https://doi.org/10.1007/978-3-319-73618-1_38
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