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• W2527645446 abstract "Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM's performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks." @default.
• W2527645446 created "2016-10-07" @default.
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• W2527645446 date "2016-09-27" @default.
• W2527645446 modified "2023-09-27" @default.
• W2527645446 title "Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks" @default.
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