Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
| Title: | Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks |
|---|---|
| Authors: | Cornegruta, Savelie, Bakewell, Robert, Withey, Samuel, Montana, Giovanni |
| Publication Year: | 2016 |
| Collection: | Computer Science Statistics |
| Subject Terms: | Computer Science - Computation and Language, Statistics - Machine Learning |
| More Details: | 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. Comment: LOUHI 2016 conference proceedings |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/1609.08409 |
| Accession Number: | edsarx.1609.08409 |
| Database: | arXiv |
| Description not available. |