# A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation

arXiv: https://arxiv.org/abs/2403.16129 · HTML: https://mlsanity.com/paper/2403.16129/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Evaluating and Calibrating Uncertainty Prediction in Regression Tasks](https://mlsanity.com/paper/1905.11659/) (2019)
- [Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation](https://mlsanity.com/paper/1905.05677/) (2019)
- [Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation](https://mlsanity.com/paper/2310.14025/) (2023)
- [Multilingual Word Sense Disambiguation with Unified Sense Representation](https://mlsanity.com/paper/2210.07447/) (2022)
- [Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation](https://mlsanity.com/paper/2305.13119/) (2023)
- [Word sense extension](https://mlsanity.com/paper/2306.05609/) (2023)
- [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)
- [Semantic Specialization for Knowledge-based Word Sense Disambiguation](https://mlsanity.com/paper/2304.11340/) (2023)
- [How Contentious Terms About People and Cultures are Used in Linked Open Data](https://mlsanity.com/paper/2311.10757/) (2023)

## Built on by (most cited first, 5 of 5)

- [IndoNLP 2025: Shared Task on Real-Time Reverse Transliteration for Romanized Indo-Aryan languages](https://mlsanity.com/paper/2501.05816/) (2025)
- [ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task](https://mlsanity.com/paper/2407.20663/) (2024)
- [Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation](https://mlsanity.com/paper/2411.18337/) (2024)
- [Visual Word Sense Disambiguation with CLIP through Dual-Channel Text Prompting and Image Augmentations](https://mlsanity.com/paper/2602.06799/) (2026)
- [TextTeacher: What Can Language Teach About Images?](https://mlsanity.com/paper/2605.22098/) (2026)

## Abstract (arXiv)

This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models. Focusing extensively on Word Sense Disambiguation (WSD), it outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet. The paper introduces cutting-edge methodologies like word sense extension (WSE) and neuromyotonic approaches, enhancing disambiguation accuracy by predicting new word senses. It examines specific applications in biomedical disambiguation and language specific optimisation and discusses the significance of cognitive metaphors in discourse analysis. The research identifies persistent challenges in the field, such as the scarcity of sense annotated corpora and the complexity of informal clinical texts. It concludes by suggesting future directions, including using large language models, visual WSD, and multilingual WSD systems, emphasising the ongoing evolution in addressing lexical complexities in NLP. This thinking perspective highlights the advancement in this field to enable computers to understand language more accurately.