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Found 4 papers, 1 papers with code
Co-Teaching Student-Model through Submission Results of Shared Task
In this research, we propose a scheme to utilize all those systems which participated in the shared tasks.
Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities
The training data were provided by Japanese categorization and the language links, and the task was to categorize the Wikipedia pages into 30 languages, with no language links from Japanese Wikipedia (20M pages in total).
SHINRA2020-ML: Categorizing 30-language Wikipedia into fine-grained NE based on "``Resource by Collaborative Contribution" scheme
In our "Resource by Collaborative Contribution (RbCC)" scheme, we conducted a shared task of structuring Wikipedia to attract participants but simultaneously submitted results are used to construct a knowledge base.
SHINRA: Structuring Wikipedia by Collaborative Contribution
We believe this situation can be improved by the following changes: 1. designing the shared-task to construct knowledge base rather than evaluating only limited test data 2. making the outputs of all the systems open to public so that we can run ensemble learning to create the better results than the best systems 3. repeating the task so that we can run the task with the larger and better training data from the output of the previous task (bootstrapping and active learning) We conducted “SHINRA2018” with the above mentioned scheme and in this paper we report the results and the future directions of the project.
