Learning multiple distributed prototypes of semantic categories for named entity recognition
2015 (English)In: International Journal of Data Mining and Bioinformatics, ISSN 1748-5681, Vol. 13, no 4, 395-411 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2015. Vol. 13, no 4, 395-411 p.
distributional semantics, semantic space ensembles, random indexing, named entity recognition, electronic health records, de-identification
Computer Science Language Technology (Computational Linguistics)
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-122461DOI: 10.1504/IJDMB.2015.072766ISI: 000366135400005OAI: oai:DiVA.org:su-122461DiVA: diva2:866458
ProjectsHigh-Performance Data Mining for Drug Effect Detection
FunderSwedish Foundation for Strategic Research , IIS11-0053