Integrating Text Embedding with Traditional NLP Features for Clinical Relation Extraction

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dc.contributor.authorHasan, Fatema
dc.contributor.authorRoy, Arpita
dc.contributor.authorPan, Shimei
dc.date.accessioned2021-01-27T19:00:43Z
dc.date.available2021-01-27T19:00:43Z
dc.description.abstractRecently, text embedding techniques such as Word2Vec and BERT have produced state-of-the-art results in a wide variety of NLP tasks. As a result, traditional NLP features frequently used in Information Extraction (IE) such as POS tags, dependency relations and semantic types have received less attention. In this paper, we investigate whether traditional NLP features can be combined with word and sentence embeddings to improve relation extraction. We have explored diverse feature sets and different neural network architectures and evaluated our models on a benchmark clinical text dataset. Our new models significantly outperformed all the baselines on the same dataset.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9288209en_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedings postprintsen_US
dc.identifierdoi:10.13016/m2ps6x-n9yb
dc.identifier.citationF. Hasan, A. Roy and S. Pan, "Integrating Text Embedding with Traditional NLP Features for Clinical Relation Extraction," 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), Baltimore, MD, USA, 2020, pp. 418-425, doi: 10.1109/ICTAI50040.2020.00072.en_US
dc.identifier.urihttps://doi.org/10.1109/ICTAI50040.2020.00072
dc.identifier.urihttp://hdl.handle.net/11603/20635
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
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dc.rights© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleIntegrating Text Embedding with Traditional NLP Features for Clinical Relation Extractionen_US
dc.typeTexten_US

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