# A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph

arXiv: https://arxiv.org/abs/2101.02875 · HTML: https://mlsanity.com/paper/2101.02875/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation](https://mlsanity.com/paper/1905.05677/) (2019)
- [Pykg2vec: A Python Library for Knowledge Graph Embedding](https://mlsanity.com/paper/1906.04239/) (2019)
- [Knowledge-based Word Sense Disambiguation using Topic Models](https://mlsanity.com/paper/1801.01900/) (2018)
- [The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD](https://mlsanity.com/paper/1805.04277/) (2018)
- [Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet](https://mlsanity.com/paper/2006.12106/) (2020)

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

- [A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation](https://mlsanity.com/paper/2403.16129/) (2024)
- [Fully Hyperbolic Rotation for Knowledge Graph Embedding](https://mlsanity.com/paper/2411.03622/) (2024)

## Abstract (arXiv)

Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human's natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local context between consecutive terms, human knowledge about terms, and a document's main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms' order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.