Contributed by Fanchao Qi.
Sememes are defined as the minimum indivisible semantic units of meaning. Some people believe that semantic meanings of all the concepts including words can be composed of a limited closed set of sememes.
As a result, sememes can help us comprehend human languages better. In fact, some works have proved that some neural NLP models perform better with incorporation of sememe knowledge.
HowNet, initially published in 2000, is the most famous sememe-based linguistic knowledge base.
- Theoretical Findings of HowNet. Zhendong Dong, Qiang Dong and Changling Hao. JCIP 2007. [pdf (Chinese)]
This paper gives a summary of theoretical findings about knowledge of HowNet.
- HowNet - a hybrid language and knowledge resource. Zhendong Dong and Qiang Dong. NLP-KE 2003. [pdf]
This paper gives a brief introduction to HowNet.
- Introduction to HowNet. Zhendong Dong and Qiang Dong. [pdf (English)] [pdf (Chinese)]
This paper gives an overall introduction to HowNet, including its features, philosophy and constructing method.
- KDML — 知网知识系统描述语言. Zhendong Dong and Qiang Dong. [pdf (Chinese)]
This paper gives a detailed introduction to Knowledge Database Mark-up Language, the mark-up language used in HowNet.
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Cross-lingual Lexical Sememe Prediction. Fanchao Qi, Yankai Lin, Maosong Sun, Hao Zhu, Ruobing Xie and Zhiyuan Liu. EMNLP 2018. [pdf] [codes]
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Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions. Wei Li, Xuancheng Ren, Damai Dai, Yunfang Wu, Houfeng Wang and Xu Sun. arXiv 2018. [pdf] [codes]
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Extended HowNet 2.0 – An Entity-Relation Common-Sense Representation Model. Wei-Yun Ma and Yueh-Yin Shih. LREC 2018. [pdf]
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Incorporating Chinese Characters of Words for Lexical Sememe Prediction. Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin and Leyu Lin. ACL 2018. [pdf] [codes]
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Lexical Sememe Prediction via Word Embeddings and Matrix Factorization. Ruobing Xie, Xingchi Yuan, Zhiyuan Liu and Maosong Sun. IJCAI 2017. [pdf] [codes]
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E-HowNet and Automatic Construction of a Lexical Ontology. Wei-Te Chen, Su-Chu Lin, Shu-Ling Huang, You-Shan Chung and Keh-Jiann Chen. COLING 2010. [pdf]
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Extended-HowNet- A Representational Framework for Concepts. Keh-Jiann Chen, Shu-Ling Huang, Yueh-Yin Shih and Yi-Jun Chen. OntoLex 2005. [pdf]
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Language Modeling with Sparse Product of Sememe Experts. Yihong Gu, Jun Yan, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin and Leyu Lin. EMNLP 2018. [pdf] [codes]
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Chinese LIWC Lexicon Expansion via Hierarchical Classification of Word Embeddings with Sememe Attention. Xiangkai Zeng, Cheng Yang, Cunchao Tu, Zhiyuan Liu and Maosong Sun. AAAI 2018. [pdf] [codes]
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Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet. Shu Liu, Jingjing Xu, Xuancheng Ren and Xu Sun. arXiv 2018. [pdf]
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Improved Word Representation Learning with Sememes. Yilin Niu, Ruobing Xie, Zhiyuan Liu and Maosong Sun. ACL 2017. [pdf] [codes]
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Embedding for Words and Word Senses Based on Human Annotated Knowledge Base: A Case Study on HowNet. Maosong Sun and Xinxiong Chen. JCIP 2016. [pdf (Chinese)]
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Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Xianghua Fu, Guo Li, Yanyan Guo and Zhiqiang Wang. Knowledge-Based Systems 2013. [pdf]
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Method of discriminant for Chinese sentence sentiment orientation based on HowNet. Lei Dang and Lei Zhang. Application Research of Computers 2010. [pdf (Chinese)]
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Semantic orientation computing based on HowNet. Yanlan Zhu, Jin Min, Yaqian Zhou, Xuanjing Huang and Lide Wu. JCIP 2005. [pdf (Chinese)]
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Chinese word sense disambiguation using HowNet. Yuntao Zhang, Ling Gong and Yongcheng Wang. ICNC 2005. [pdf]
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Word Similarity Computing Based on HowNet. Qun Liu and Sujian Li. International Journal of Computational Linguistics & Chinese Language Processing 2002. [pdf (Chinese)]