# Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets

arXiv: https://arxiv.org/abs/1912.01795 · HTML: https://mlsanity.com/paper/1912.01795/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)
- [Automatic Construction of Sememe Knowledge Bases via Dictionaries](https://mlsanity.com/paper/2105.12585/) (2021)
- [Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information](https://mlsanity.com/paper/2203.07426/) (2022)
- [The Analysis about Building Cross-lingual Sememe Knowledge Base Based on Deep Clustering Network](https://mlsanity.com/paper/2208.05462/) (2022)
- [Reversible Jump Attack to Textual Classifiers with Modification Reduction](https://mlsanity.com/paper/2403.14731/) (2024)
- [SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation](https://mlsanity.com/paper/2406.10297/) (2024)

## Abstract (arXiv)

A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain words annotated with sememes, have been successfully applied to many NLP tasks. However, existing sememe KBs are built on only a few languages, which hinders their widespread utilization. To address the issue, we propose to build a unified sememe KB for multiple languages based on BabelNet, a multilingual encyclopedic dictionary. We first build a dataset serving as the seed of the multilingual sememe KB. It manually annotates sememes for over 15 thousand synsets (the entries of BabelNet). Then, we present a novel task of automatic sememe prediction for synsets, aiming to expand the seed dataset into a usable KB. We also propose two simple and effective models, which exploit different information of synsets. Finally, we conduct quantitative and qualitative analyses to explore important factors and difficulties in the task. All the source code and data of this work can be obtained on https://github.com/thunlp/BabelNet-Sememe-Prediction.