【论文整理】义原计算必读论文x

　【论文整理】义原计算必读论文 Must-read Papers on Sememe Computation Contributed by Fanchao Qi. Introduction A sememe is defined as the minimum indivisible semantic unit in linguistics. Some linguists believe that meanings of all the concepts including words can be composed of a limited set of sememes. Sememes can help us comprehend human languages better. Some studies have proved that neural NLP models perform better with incorporation of sememe knowledge. HowNet, initially published in 2000, is the most famous sememe-based linguistic knowledge base. Papers Introduction to HowNet 1. 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. 2. HowNet - a hybrid language and knowledge resource. Zhendong Dong and Qiang Dong. NLP-KE 2003. [pdf] This paper gives a brief introduction to HowNet. 3. 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. 4. KDML — Knowledge Database Mark-up Language. 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. Expansion of sememe-based knowledge bases 1. Cross-lingual Lexical Sememe Prediction. Fanchao Qi, Yankai Lin, Maosong Sun, Hao Zhu, Ruobing Xie and Zhiyuan Liu. EMNLP 2018. [pdf] [code]

　2. 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] [code] 3. Extended HowNet 2.0: An Entity-Relation Common-Sense Representation Model. Wei-Yun Ma and Yueh-Yin Shih. LREC 2018. [pdf] 4. 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] [code] 5. Lexical Sememe Prediction via Word Embeddings and Matrix Factorization. Ruobing Xie, Xingchi Yuan, Zhiyuan Liu and Maosong Sun. IJCAI 2017. [pdf] [code] 6. 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] 7. Extended-HowNet: A Representational Framework for Concepts. Keh-Jiann Chen, Shu-Ling Huang, Yueh-Yin Shih and Yi-Jun Chen. OntoLex 2005. [pdf] Applications 1. Modeling Semantic Compositionality with Sememe Knowledge. Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu and Maosong Sun. ACL 2019. [pdf] [code] 2. Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge. Ziran Li, Ning Ding, Zhiyuan Liu, Haitao Zheng and Ying Shen. ACL 2019. [pdf] [code] 3. Unsupervised Neural Aspect Extraction with Sememes. Ling Luo, Xiang Ao, Yan Song, Jinyao Li, Xiaopeng Yang, Qing He and Dong Yu. IJCAI 2019. [pdf] 4. K-BERT: Enabling Language Representation with Knowledge Graph. Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng and Ping Wang. arXiv 2019. [pdf] 5. Semantic Representation Learning Based on HowNet. Jingwen Zhu, Yuji Yang, Bin Xu and Juanzi Li. JCIP 2019. [pdf (Chinese)] 6. A Word Representation Method Based on Hownet. Yang Chen and Zhiyong Luo. Acta Scientiarum Naturalium Universitatis Pekinensis 2019. [pdf (Chinese)] 7. Lnguage 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] [code]

　8. 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] [code] 9. 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] 10. Improved Word Representation Learning with Sememes. Yilin Niu, Ruobing Xie, Zhiyuan Liu and Maosong Sun. ACL 2017. [pdf] [code] 11. 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)] 12. 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] 13. Employing Morphological Structuresand Sememes for Chinese Event Extraction. Peifeng Li and Guodong Zhou. COLING 2012. [pdf] 14. Method of discriminant for Chinese sentence sentiment orientation based on HowNet. Lei Dang and Lei Zhang. Application Research of Computers 2010. [pdf (Chinese)] 15. Word Sense Disambiguation through Sememe Labeling. Xiangyu Duan, Jun Zhao and Bo Xu. IJCAI 2007. [pdf] 16. Analogy Generation with HowNet. Tony Veale. IJCAI 2005. [pdf] 17. Semantic orientation computing based on HowNet. Yanlan Zhu, Jin Min, Yaqian Zhou, Xuanjing Huang and Lide Wu. JCIP 2005. [pdf (Chinese)] 18. Chinese word sense disambiguation using HowNet. Yuntao Zhang, Ling Gong and Yongcheng Wang. ICNC 2005. [pdf] 19. Word Similarity Computing Based on HowNet. Qun Liu and Sujian Li. International Journal of Computational Linguistics & Chinese Language Processing 2002. [pdf (Chinese)]