Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.06386

Authors: Kikuchi, Masato, Ono, Masatsugu, Soga, Toshioki, Tanabe, Tetsu, Ozono, Tadachika

Institutions: Nagoya Institute of Technology, Muroran Institute of Technology, Chitose Institute of Science and Technology, Hokkaido University of Science, Hokkaido University of Education, Hokkaido University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403623461

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