# Knowledge-based Word Sense Disambiguation using Topic Models

arXiv: https://arxiv.org/abs/1801.01900 · HTML: https://mlsanity.com/paper/1801.01900/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

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

- [Incorporating Word Sense Disambiguation in Neural Language Models](https://mlsanity.com/paper/2106.07967/) (2021)
- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph](https://mlsanity.com/paper/2101.02875/) (2021)
- [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)

## Abstract (arXiv)

Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.