Abstract
Antonym detection is a vital task in NLP systems. Pattern-based methods, typical solutions for this, recognize semantic relationships between words using given patterns but have limited performance. Distributed word embeddings often struggle to distinguish antonyms from synonyms because their representations rely on local co-occurrences in similar contexts. Combining the ambiguity of Chinese and the contradictory nature of antonyms, antonym detection faces unique challenges. In this paper, we propose a word-sememe graph to integrate relationships between sememes and Chinese words, organized as a 4-partite graph. We design a heuristic sememe relevance computation as a supplementary measure and develop a relation inference scheme using related sememes as taxonomic information to leverage the relational transitivity. The 4-partite graph can be extended based on this scheme. We introduce the R elation D iscriminated L earning based on S ememe A ttention (RDLSA) model, employing three attention strategies on sememes to learn flexible entity representations. Antonym relations are detected using a Link Prediction approach with these embeddings. Our method demonstrates superior performance in Triple Classification and Chinese Antonym Detection compared to the baselines. Experimental results show reduced ambiguity and improved antonym detection using linguistic sememes. A quantitative ablation analysis further confirms our scheme’s effectiveness in capturing antonyms.
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Notes
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Code and data: https://github.com/CGCL-codes/RDLSA.
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Acknowledgment
The research is supported by The National Natural Science Foundation of China under Grant Nos. 61932004 and 62072205.
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Following the guidelines from General ethical issues in Machine Learning (https://www.w3.org/TR/webmachinelearning-ethics/#general-ethical-issues-in-machine-learning), we will briefly describe our ethical considerations.
Our work mainly focuses on semantic mining in the Chinese domain, without Bias, Fairness, Security, Privacy, Environmental Impact, and Discrimination against a group or collective. Our method combines external professional linguistic knowledge and has good Transparency and Interpretability.
Our data is sourced from publicly available resources on the internet and all references are cited in the paper, such as Github and other data that follows open-source licenses. Our data does not involve any personal privacy or inference of personal information. Our work is dedicated to researching the potential semantic relationships between words in Chinese and does not have any police or military applications.
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Zhang, Z., Yuan, P., Jin, H. (2023). Exploring Word-Sememe Graph-Centric Chinese Antonym Detection. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_35
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