# Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries

arXiv: https://arxiv.org/abs/2409.06386 · HTML: https://mlsanity.com/paper/2409.06386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation](https://mlsanity.com/paper/1905.05677/) (2019)
- [Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories](https://mlsanity.com/paper/2110.14091/) (2021)

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

- [CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning](https://mlsanity.com/paper/2510.18466/) (2025)
- [English Word Sense Disambiguation in 2026: When the Labels Become the Bottleneck](https://mlsanity.com/paper/2609.17554/) (2026)

## Abstract (arXiv)

WordNet is one of the largest handcrafted concept dictionaries visualizing word connections through semantic relationships. It is widely used as a word sense inventory in natural language processing tasks. However, WordNet's fine-grained senses have been criticized for limiting its usability. In this paper, we semantically match sense definitions from Cambridge dictionaries and WordNet and develop new coarse-grained sense inventories. We verify the effectiveness of our inventories by comparing their semantic coherences with that of Coarse Sense Inventory. The advantages of the proposed inventories include their low dependency on large-scale resources, better aggregation of closely related senses, CEFR-level assignments, and ease of expansion and improvement.