# Incorporating Glosses into Neural Word Sense Disambiguation

arXiv: https://arxiv.org/abs/1805.08028 · HTML: https://mlsanity.com/paper/1805.08028/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 18 of 18)

- [Efficient Zero-shot Event Extraction with Context-Definition Alignment](https://mlsanity.com/paper/2211.05156/) (2022)
- [Incorporating Word Sense Disambiguation in Neural Language Models](https://mlsanity.com/paper/2106.07967/) (2021)
- [Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy](https://mlsanity.com/paper/2110.02204/) (2021)
- [Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories](https://mlsanity.com/paper/2110.14091/) (2021)
- [Multilingual Word Sense Disambiguation with Unified Sense Representation](https://mlsanity.com/paper/2210.07447/) (2022)
- [Non-Parametric Few-Shot Learning for Word Sense Disambiguation](https://mlsanity.com/paper/2104.12677/) (2021)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [Word sense extension](https://mlsanity.com/paper/2306.05609/) (2023)
- [Training Bi-Encoders for Word Sense Disambiguation](https://mlsanity.com/paper/2105.10146/) (2021)
- [Context-gloss Augmentation for Improving Word Sense Disambiguation](https://mlsanity.com/paper/2110.07174/) (2021)
- [Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation](https://mlsanity.com/paper/2411.18337/) (2024)
- [SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc](https://mlsanity.com/paper/2503.05958/) (2025)
- [SensPick: Sense Picking for Word Sense Disambiguation](https://mlsanity.com/paper/2102.05260/) (2021)
- [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)
- [Automatic Construction of Sememe Knowledge Bases via Dictionaries](https://mlsanity.com/paper/2105.12585/) (2021)
- [Vec2Gloss: definition modeling leveraging contextualized vectors with Wordnet gloss](https://mlsanity.com/paper/2305.17855/) (2023)
- [An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs](https://mlsanity.com/paper/2603.05400/) (2026)
- [A Quadratic 0-1 Programming Approach for Word Sense Disambiguation](https://mlsanity.com/paper/2201.04877/) (2022)

## Abstract (arXiv)

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-theart systems on several English all-words WSD datasets.