Automated Author ProfileOzono, Tadachika
Nagoya Institute of Technology0000-0003-1568-8832
Ozono, Tadachika
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.2 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
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.PublicationThese 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.
Authors
- Kikuchi, Masato ;
- Ono, Masatsugu ;
- Soga, Toshioki ;
- Tanabe, Tetsu ;
- Ozono, Tadachika
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.PublicationThese 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.
Authors
- Kikuchi, Masato ;
- Ono, Masatsugu ;
- Soga, Toshioki ;
- Tanabe, Tetsu ;
- Ozono, Tadachika