End-to-End Neural Entity Linking
| Autor: | Thomas Hofmann, Nikolaos Kolitsas, Octavian-Eugen Ganea |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Training set Computer Science - Computation and Language Computer science business.industry Probabilistic logic 020207 software engineering 02 engineering and technology computer.software_genre Task (project management) Information extraction Annotation Entity linking End-to-end principle 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | CoNLL |
| Popis: | Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy. Full paper at CoNLL 2018: Conference on Computational Natural Language Learning |
| Databáze: | OpenAIRE |
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