# Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation

arXiv: https://arxiv.org/abs/1905.05677 · HTML: https://mlsanity.com/paper/1905.05677/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 17 of 17)

- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [PolyLM: Learning about Polysemy through Language Modeling](https://mlsanity.com/paper/2101.10448/) (2021)
- [A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation](https://mlsanity.com/paper/2403.16129/) (2024)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization](https://mlsanity.com/paper/2404.08685/) (2024)
- [Evaluation Of Word Embeddings From Large-Scale French Web Content](https://mlsanity.com/paper/2105.01990/) (2021)
- [Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Language](https://mlsanity.com/paper/2201.10262/) (2022)
- [Word Sense Disambiguation as a Game of Neurosymbolic Darts](https://mlsanity.com/paper/2307.16663/) (2023)
- [A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph](https://mlsanity.com/paper/2101.02875/) (2021)
- [Training Bi-Encoders for Word Sense Disambiguation](https://mlsanity.com/paper/2105.10146/) (2021)
- [Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries](https://mlsanity.com/paper/2409.06386/) (2024)
- [SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc](https://mlsanity.com/paper/2503.05958/) (2025)
- [EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses](https://mlsanity.com/paper/2103.00232/) (2021)
- [Crossword: A Semantic Approach to Data Compression via Masking](https://mlsanity.com/paper/2304.01106/) (2023)
- [PolyBERT: Fine-Tuned Poly Encoder BERT-Based Model for Word Sense Disambiguation](https://mlsanity.com/paper/2506.00968/) (2025)
- [A Quadratic 0-1 Programming Approach for Word Sense Disambiguation](https://mlsanity.com/paper/2201.04877/) (2022)
- [Learning Homographic Disambiguation Representation for Neural Machine Translation](https://mlsanity.com/paper/2304.05860/) (2023)

## Abstract (arXiv)

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database. We propose two different methods that greatly reduces the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision. In addition to our method, we present a WSD system which relies on pre-trained BERT word vectors in order to achieve results that significantly outperform the state of the art on all WSD evaluation tasks.