# Modeling Semantic Compositionality with Sememe Knowledge

arXiv: https://arxiv.org/abs/1907.04744 · HTML: https://mlsanity.com/paper/1907.04744/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 8 of 8)

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- [Towards Equipping Transformer with the Ability of Systematic Compositionality](https://mlsanity.com/paper/2312.07280/) (2023)
- [Automatic Construction of Sememe Knowledge Bases via Dictionaries](https://mlsanity.com/paper/2105.12585/) (2021)
- [Semantic Sentence Composition Reasoning for Multi-Hop Question Answering](https://mlsanity.com/paper/2203.00160/) (2022)
- [Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information](https://mlsanity.com/paper/2203.07426/) (2022)

## Abstract (arXiv)

Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememeincorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC. All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.