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A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records
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In: Yearb Med Inform (2021)
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From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
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In: Trans Assoc Comput Linguist (2018)
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Abstract:
This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.
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Article
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URL: https://doi.org/10.1162/tacl_a_00025
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7236559/
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