# Chinese Word Sense Embedding with SememeWSD and Synonym Set

arXiv: https://arxiv.org/abs/2206.14388 · HTML: https://mlsanity.com/paper/2206.14388/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks](https://mlsanity.com/paper/1909.06627/) (2019)
- [OpenHowNet: An Open Sememe-based Lexical Knowledge Base](https://mlsanity.com/paper/1901.09957/) (2019)
- [Multitask Learning for Fine-Grained Twitter Sentiment Analysis](https://mlsanity.com/paper/1707.03569/) (2017)
- [An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages](https://mlsanity.com/paper/1804.10686/) (2018)

## Built on by (most cited first, 2 of 2)

- [Dynamic Fair Federated Learning Based on Reinforcement Learning](https://mlsanity.com/paper/2311.00959/) (2023)
- [Epidemic Decision-making System Based Federated Reinforcement Learning](https://mlsanity.com/paper/2311.01749/) (2023)

## Abstract (arXiv)

Word embedding is a fundamental natural language processing task which can learn feature of words. However, most word embedding methods assign only one vector to a word, even if polysemous words have multi-senses. To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet. We use the SememeWSD model, an unsupervised word sense disambiguation model based on OpenHowNet, to do word sense disambiguation and annotate the polysemous word with sense id. Then, we obtain top 10 synonyms of the word sense from OpenHowNet and calculate the average vector of synonyms as the vector of the word sense. In experiments, We evaluate the SWSDS model on semantic similarity calculation with Gensim's wmdistance method. It achieves improvement of accuracy. We also examine the SememeWSD model on different BERT models to find the more effective model.