Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

North American Chapter of the Association for Computational Linguistics, pp. 1142-1150, 2019.

Cited by: 4|Bibtex|Views71|DOI:https://doi.org/10.18653/v1/n19-1117
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., emph{person} or emph{location}) a...More

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