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

Abstract (our paper)

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. Our inventories are publicly available for free use.

Publication

These datasets are part of our research results. If you make use of our datasets, please cite:

Masato Kikuchi, Masatsugu Ono, Toshioki Soga, Tetsu Tanabe, Tadachika Ozono. Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries. In Proceedings of the 11th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA 2024). 6 pages, 2024.