An entity-aware extension of ELMo, E-ELMo, predicts gold entities at mention positions and powers a local entity disambiguation model that achieves state-of-the-art results on AIDA and TAC 2010.
One for All: Towards Language Independent Named Entity Linking
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abstract
Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, English, with less attention being paid to other languages, such as Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent Entity Linking system, which provides an EL framework which, once trained on one language, works remarkably well on a number of different languages without change. LIEL makes a joint global prediction over the entire document, employing a discriminative reranking framework with many domain and language-independent feature functions. Experiments on numerous benchmark datasets, show that the proposed system, once trained on one language, English, outperforms several state-of-the-art systems in English (by 4 points) and the trained model also works very well on Spanish (14 points better than a competitor system), demonstrating the viability of the approach.
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2019 1verdicts
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Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
An entity-aware extension of ELMo, E-ELMo, predicts gold entities at mention positions and powers a local entity disambiguation model that achieves state-of-the-art results on AIDA and TAC 2010.