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

TL;DR

This paper introduces a method to create coarse-grained sense inventories by semantically matching definitions from dictionaries, improving usability and semantic coherence over traditional fine-grained inventories like WordNet.

Contribution

It proposes a novel approach to generate coarse sense inventories through semantic matching, reducing reliance on large resources and enhancing sense aggregation.

Findings

Inventories show higher semantic coherence than WordNet

Better aggregation of related senses achieved

CEFR-level sense assignments are feasible

Abstract

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.