NDLI: Active Learning with Visualization for Text Data

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Active Learning with Visualization for Text Data

Content Provider	ACM Digital Library
Author	Huang, Lulu Minghim, Rosane Matwin, Stan de Carvalho, Eder J.
Abstract	Labeled datasets are always limited, and oftentimes the quantity of labeled data is a bottleneck for data analytics. This especially affects supervised machine learning methods, which require labels for models to learn from the labeled data. Active learning algorithms have been proposed to help achieve good analytic models with limited labeling efforts, by determining which additional instance labels will be most beneficial for learning for a given model. Active learning is consistent with interactive analytics as it proceeds in a cycle in which the unlabeled data is automatically explored. However, in active learning users have no control of the instances to be labeled, and for text data, the annotation interface is usually document only. Both of these constraints seem to affect the performance of an active learning model. We hypothesize that visualization techniques, particularly interactive ones, will help to address these constraints. In this paper, we implement a pilot study of visualization in active learning for text classification, with an interactive labeling interface. We compare the results of three experiments. Early results indicate that visualization improves high-performance machine learning model building with an active learning algorithm.
Starting Page	69
Ending Page	74
Page Count	6
File Format	PDF
ISBN	9781450349031
DOI	10.1145/3038462.3038469
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Visualization Active learning Text classification
Content Type	Text
Resource Type	Article

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