| Título |
The Combination of BERT and Data Oversampling for Answer Type Prediction |
| Tipo |
Congreso |
| Sub-tipo |
Indefinido |
| Descripción |
2nd SeMantic Answer Type and Relation Prediction Task at ISWC Semantic Web Challenge, SMART 2021 |
| Resumen |
In this paper, we address the Task 1 (of the SMART Task 2021) of predicting the answer categories and types based on target ontologies, which could be useful in knowledge-based Question Answering (QA) systems. We introduced our method by combining the power of BERT architectures with data oversampling via replacements of linked terms to Wikidata and dependent noun phrases to attain the state-of-the-art performance. The accuracy on the DBpedia dataset is 98.5%, whereas NDCG@5 and NDCG@10 are 72.7% and 66.4% respectively. Our model has the best performance compared to other teams, with the accuracy score of 98% and Mean Reciprocal Rank (MRR) of 70% on the Wikidata dataset. © 2022 CEUR-WS. All rights reserved. |
| Observaciones |
CEUR Workshop Proceedings v. 3119 |
| Lugar |
Virtual, online |
| País |
Indefinido |
| No. de páginas |
|
| Vol. / Cap. |
CEUR v. 3119 |
| Inicio |
2021-10-26 |
| Fin |
|
| ISBN/ISSN |
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