Name
Title | ||
|---|---|---|
Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/SHTI190211 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Natural Language Processing,Machine Learning,Neural Networks (Computer) | Sentence boundary disambiguation,Computer science,Natural language processing,Artificial intelligence,Network model | Conference |
Volume | ISSN | Citations |
264 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin C. Knoll | 1 | 0 | 2.03 |
| Elizabeth A. Lindemann | 2 | 0 | 0.34 |
| Arian L. Albert | 3 | 0 | 0.34 |
| G B Melton | 4 | 264 | 45.72 |
| Serguei V S Pakhomov | 5 | 471 | 40.62 |