The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.
CITATION STYLE
Biswas, R., Sofronova, R., Alam, M., Heist, N., Paulheim, H., & Sack, H. (2021). Do Judge an Entity by Its Name! Entity Typing Using Language Models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12739 LNCS, pp. 65–70). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80418-3_12
Mendeley helps you to discover research relevant for your work.