Clinical Text Classification with Word Embedding Features vs. Bag-of-Words Features

Authors

Yijun Shao, The George Washington University
Stephanie Taylor, VA Greater Los Angeles Healthcare System
Nell Marshall, VA Palo Alto Health Care System
Craig Morioka, VA Greater Los Angeles Healthcare System
Qing Zeng-Treitler, The George Washington University

Document Type

Conference Proceeding

Publication Date

1-22-2019

Journal

Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

DOI

10.1109/BigData.2018.8622345

Keywords

clinical text mining; natural language processing; word embedding

Abstract

© 2018 IEEE. Word embedding motivated by deep learning have shown promising results over traditional bag-of-words features for natural language processing. When trained on large text corpora, word embedding methods such as word2vec and doc2vec methods have the advantage of learning from unlabeled data and reduce the dimension of the feature space. In this study, we experimented with word2vec and doc2vec features for a set of clinical text classification tasks and compared the results with using the traditional bag-of-words (BOW) features. The study showed that the word2vec features performed better than the BOW-1-gram features. However, when 2-grams were added to BOW, comparison results were mixed.

APA Citation

Shao, Y., Taylor, S., Marshall, N., Morioka, C., & Zeng-Treitler, Q. (2019). Clinical Text Classification with Word Embedding Features vs. Bag-of-Words Features. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, (). http://dx.doi.org/10.1109/BigData.2018.8622345