Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries
This work addresses usability issues for NLP practitioners by providing more practical sense inventories, though it is incremental as it builds on existing resources.
The paper tackled the problem of WordNet's fine-grained senses limiting usability in NLP by developing new coarse-grained sense inventories through semantic matching of Cambridge dictionaries and WordNet, resulting in advantages such as low dependency on large-scale resources and better sense aggregation.
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.