# Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

arXiv: https://arxiv.org/abs/2203.07426 · HTML: https://mlsanity.com/paper/2203.07426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution](https://mlsanity.com/paper/2106.06361/) (2021)
- [Image-embodied Knowledge Representation Learning](https://mlsanity.com/paper/1609.07028/) (2016)
- [Multi-channel Reverse Dictionary Model](https://mlsanity.com/paper/1912.08441/) (2019)
- [LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching](https://mlsanity.com/paper/2102.12671/) (2021)
- [QuoteR: A Benchmark of Quote Recommendation for Writing](https://mlsanity.com/paper/2202.13145/) (2022)
- [Modeling Semantic Compositionality with Sememe Knowledge](https://mlsanity.com/paper/1907.04744/) (2019)
- [Language Modeling with Sparse Product of Sememe Experts](https://mlsanity.com/paper/1810.12387/) (2018)
- [Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets](https://mlsanity.com/paper/1912.01795/) (2019)
- [Incorporating Chinese Characters of Words for Lexical Sememe Prediction](https://mlsanity.com/paper/1806.06349/) (2018)
- [Automatic Construction of Sememe Knowledge Bases via Dictionaries](https://mlsanity.com/paper/2105.12585/) (2021)

## Built on by (most cited first, 1 of 1)

- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)

## Abstract (arXiv)

In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been successfully applied to various NLP tasks. However, existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. To address this issue, the task of sememe prediction for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically predicting sememes for a BabelNet synset, the words in many languages in the synset would obtain sememe annotations simultaneously. However, previous SPBS methods have not taken full advantage of the abundant information in BabelNet. In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS. We design a multimodal information fusion model to encode and combine this information for sememe prediction. Experimental results show the substantial outperformance of our model over previous methods (about 10 MAP and F1 scores). All the code and data of this paper can be obtained at https://github.com/thunlp/MSGI.