Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set
This work addresses the need for better structured knowledge in NLP by providing a new dataset, but it is incremental as it builds on existing ENE tag sets and classification methods.
The authors tackled the problem of structuring Wikipedia articles by introducing SHINRA-5LDS, a large multi-lingual and multi-labeled dataset annotated with an Extended Named Entity tag set, and found that existing classification models struggle with fine-grained tags on such datasets.
Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions. Structuring Wikipedia is the initial step towards this goal which can facilitate fine-grain classification of articles. In this work, we introduce the Shinra 5-Language Categorization Dataset (SHINRA-5LDS), a large multi-lingual and multi-labeled set of annotated Wikipedia articles in Japanese, English, French, German, and Farsi using Extended Named Entity (ENE) tag set. We evaluate the dataset using the best models provided for ENE label set classification and show that the currently available classification models struggle with large datasets using fine-grained tag sets.