Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Autor:	Knoll, Benjamin C., Lindemann, Elizabeth A., Albert, Arian L., Melton, Genevieve B., Pakhomov, Serguei V. S.
Předmět:	RECURRENT neural networks NATURAL language processing APPLICATION program interfaces CORPORA MACHINE learning
Zdroj:	Medinfo; 2019, p198-202, 5p
Abstrakt:	Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi- LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.com/NLPIE/clinical-sentences. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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