Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs

WS 2016  ·  Simon Almgren, Sean Pavlov, Olof Mogren ·

We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60{\%} improvement in F 1 score over the baseline, and our system generalizes well between different datasets.

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