DeepBioWSD: effective deep neural word sense disambiguation of biomedical text data.

ABSTRACT:

Objective

In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties in the natural language processing applications pipeline. Supervised WSD algorithms largely outperform un- or semisupervised and knowledge-based methods; however, they train 1 separate classifier for each ambiguous term, necessitating a large number of expert-labeled training data, an unattainable goal in medical informatics. To alleviate this need, a single model that shares statistical strength across all instances and scales well with the vocabulary size is desirable.

Materials and methods

Built on recent advances in deep lear

SUBMITTER: Pesaranghader A 

PROVIDER: S-EPMC7787358 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature