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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
References
Wu, Y., Zhang, M.: Overview of the NLPCC 2017 shared task: Chinese word semantic relation classification. In: The 6th Conference on Natural Language Processing and Chinese Computing, Dalian, China, 8–12 November 2017
Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., Ó Séaghdha, D., Padó, S., Pennacchiotti, M., Romano, L., Szpakowicz, S.: Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, pp. 94–99. Association for Computational Linguistics (2009)
Hashimoto, K., Stenetorp, P., Miwa, M., Tsuruoka, Y.: Task-oriented learning of word embeddings for semantic relation classification. arXiv preprint arXiv:1503.00095 (2015)
dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. arXiv preprint arXiv:1504.06580 (2015)
Silva, V.S., Hürliman, M., Davis, B., Handschuh, S., Freitas, A.: Semantic relation classification: task formalisation and refinement. In: COLING 2016, p. 30 (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, pp. 3111–3119. Curran Associates Inc., Red Hook (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP, vol. 14, pp. 1532–1543 (2014)
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: ACL, vol. 1, pp. 1555–1565 (2014)
Guo, S., Guan, Y., Li, R., Zhang, Q.: Chinese word similarity computing based on combination strategy. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC-2016. LNCS (LNAI), vol. 10102, pp. 744–752. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_67
Pei, J., Zhang, C., Huang, D., Ma, J.: Combining word embedding and semantic lexicon for chinese word similarity computation. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC-2016. LNCS (LNAI), vol. 10102, pp. 766–777. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_69
Zhao, J., Zhu, T., Lan, M.: ECNU: one stone two birds: ensemble of heterogenous measures for semantic relatedness and textual entailment. In: SemEval@ COLING, pp. 271–277 (2014)
Qiu, X., Gong, J., Huang, X.: Overview of the NLPCC 2017 shared task: Chinese news headline categorization. arXiv:1706.02883v1 (2017)
Jiaju, M., Yiming, Z., Yunqi, G., Hong-Xiang, Y.: Tongyici Cilin. ShangHai Dictionary Publication (1983)
Dong, Z., Dong, Q., Hao, C.: Hownet and the Computation of Meaning. World Scientific, Singapore (2006)
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.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-73618-1_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73617-4
Online ISBN: 978-3-319-73618-1
eBook Packages: Computer ScienceComputer Science (R0)