---
title: "Enhancing Lexical Relation Mining with Structured Sememe"
description: "They then introduce SememeLRM, which encodes these trees with a relational graph network (R-GNN) and passes the result… 72 references."
authors: ["Hansi Wang", "Qiliang Liang", "Yue Wang", "Yang Liu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.224/"
doi: "10.18653/v1/2026.acl-long.224"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-enhancing-lexical-relation-mining-structured/"
---
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# Enhancing Lexical Relation Mining with Structured Sememe Knowledge

**Authors:** Hansi Wang, Qiliang Liang, [Yue Wang](https://md.research.elamur.ai/free/en/authors/yue-wang/) and [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.224/) · [PDF](https://aclanthology.org/2026.acl-long.224.pdf) · [DOI 10.18653/v1/2026.acl-long.224](https://doi.org/10.18653/v1/2026.acl-long.224) · [OpenAlex](https://openalex.org/W7166860384)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles Lexical Relation Mining (LRM) in two settings. Lexical Relation Classification (LRC) labels word pairs with relations such as synonymy or hypernymy. Lexical Entailment (LE) scores how strongly one word entails another. Current PLM-based systems still mix up subtle relations, for example synonymy versus hypernymy.

The authors draw on HowNet, where each word sense is described as a tree of sememes (minimal meaning units). Their idea is that these internal structures mirror relations between words. Many words in benchmark datasets have no HowNet annotation, so they first develop a pipeline for automatic Sememe Tree Construction (STC), which predicts a tree's nodes, edges and edge types. They then introduce SememeLRM, which encodes these trees with a relational graph network (R-GNN) and passes the result into a PLM.

Across benchmarks, the method improves results by 1.6% on average. On most benchmarks it also beats LLM-based baselines roughly twenty times its size. Trees produced automatically perform about as well as gold HowNet trees. This shows structured sememe knowledge can be used even where annotations are missing.

## Key points
- HowNet covers 100,000+ English and Chinese words using about 2,000 sememes. OpenHowNet supplies the tree-structured version, and the work focuses on English.
- Average scores (LRC / LE):
  - DeBERTa-large baseline: 88.7 / 89.1
  - Adding WordNet glosses: 88.8 / 89.4
  - SememeLRM: 90.3 / 89.8
- Sememe inputs are compact. Each sample sends 13.3 sememe tokens to the R-GNN and 4.3 sense tokens to the PLM, versus 95.4 tokens when glosses are used.
- In the ablations, tree structure provides most of the gain:
  - Flat sememe set: 89.2 / 89.5
  - GCN with trees but no edge types: 89.9 / 89.9
  - R-GAT: 90.1 / 90.1
  - R-GCN: 90.3 / 89.8
- Code and resources are available at github.com/COOLPKU/SememeLRM. Stated limitations are low-resource languages, coarsened edge types and extra training cost from the R-GNN.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 244 works · 39,359 citations

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🧭 Topics (1): [📖 Lexical semantics & word embeddings](https://md.research.elamur.ai/free/en/topics/lexical-semantics/) · 63 works

## Cite

Cite as: Hansi Wang, Qiliang Liang, Yue Wang and Yang Liu. 2026. Enhancing Lexical Relation Mining with Structured Sememe Knowledge. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026enhancing,
  title = {{Enhancing Lexical Relation Mining with Structured Sememe Knowledge}},
  author = {Hansi Wang, Qiliang Liang, Yue Wang and Yang Liu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.224},
  url = {https://doi.org/10.18653/v1/2026.acl-long.224}
}
```

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*Updated 1 November 2026*