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OpenHowNet: An Open Sememe-based Lexical Knowledge Base

arXiv 1901.09957 ↗First posted 28 Jan 2019cs.CLcited by 21

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Abstract (arXiv)

In this paper, we present an open sememe-based lexical knowledge base OpenHowNet. Based on well-known HowNet, OpenHowNet comprises three components: core data which is composed of more than 100 thousand senses annotated with sememes, OpenHowNet Web which gives a brief introduction to OpenHowNet as well as provides online exhibition of OpenHowNet information, and OpenHowNet API which includes several useful APIs such as accessing OpenHowNet core data and drawing sememe tree structures of senses. In the main text, we first give some backgrounds including definition of sememe and details of HowNet. And then we introduce some previous HowNet and sememe-based research works. Last but not least, we detail the constituents of OpenHowNet and their basic features and functionalities. Additionally, we briefly make a summary and list some future works.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2019.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 6; 2024: 3; 2025: 1; 2026: 1.

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  10. 2021K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answeringcited by 2
  11. 2022Chinese Word Sense Embedding with SememeWSD and Synonym Setcited by 2
  12. 2023TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognitioncited by 2
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  2. 2021Automatic Construction of Sememe Knowledge Bases via Dictionariescited by 1
  3. 2023A Concept Knowledge Graph for User Next Intent Prediction at Alipaycited by 1
  4. 2023Dynamic Multi-View Fusion Mechanism For Chinese Relation Extractioncited by 1
  5. 2021Text analysis and deep learning: A network approachcited by 0
  6. 2024A Knowledge-Injected Curriculum Pretraining Framework for Question Answeringcited by 0
  7. 2024Reversible Jump Attack to Textual Classifiers with Modification Reductioncited by 0
  8. 2024SEE: Sememe Entanglement Encoding for Transformer-bases Models Compressioncited by 0
  9. 2026LexReward: A Taxonomy-Driven Reward Framework for Legal Language Modelscited by 0