Cost-aware active learning for named entity recognition in clinical text.

ABSTRACT:

Objective

Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This study introduces a cost-aware AL method, which simultaneously models both the annotation cost and the informativeness of the samples and evaluates both via simulation and user studies.

Materials and methods

We designed a novel, cost-aware AL algorithm (Cost-CAUSE) for annotating clinical named entities; we first utilized lexical and syntactic features to estimate annotation cost, then we incorporated this cost measure into an existing AL algorithm. Using the 2010 i2b2/VA data set, we then conducted a simulation study comparing Cos

SUBMITTER: Wei Q 

PROVIDER: S-EPMC6798575 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature