Journal
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
Volume 13, Issue 4, Pages 395-411Publisher
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJDMB.2015.072766
Keywords
distributional semantics; semantic space ensembles; random indexing; named entity recognition; electronic health records; de-identification
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Funding
- project High-Performance Data Mining for Drug Effect Detection at Stockholm University - Swedish Foundation for Strategic Research [IIS11-0053]
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The scarcity of large labelled datasets comprising clinical text that can be exploited within the paradigm of supervised machine learning creates barriers for the secondary use of data from electronic health records. It is therefore important to develop capabilities to leverage the large amounts of unlabelled data that, indeed, tend to be readily available. One technique utilises distributional semantics to create word representations in a wholly unsupervised manner and uses existing training data to learn prototypical representations of predefined semantic categories. Features describing whether a given word belongs to a certain category are then provided to the learning algorithm. It has been shown that using multiple distributional semantic models, each employing a different word order strategy, can lead to enhanced predictive performance. Here, another hyperparameter is also varied - the size of the context window and an experimental investigation shows that this leads to further performance gains.
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