External knowledge and query strategies in active learning: A study in clinical information extraction
Kholghi, Mahnoosh, Sitbon, Laurianne, Zuccon, Guido, & Nguyen, Anthony (2015) External knowledge and query strategies in active learning: A study in clinical information extraction. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM, Melbourne, Australia, pp. 143-152.
This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.
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|Item Type:||Conference Paper|
|Keywords:||Domain Knowledge, Active Learning, Concept Extraction, Clinical Free Text, Conditional Random Fields|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright is held by the owner/author(s). Publication rights licensed to ACM.|
|Copyright Statement:||Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.|
|Deposited On:||21 Jul 2015 00:04|
|Last Modified:||19 May 2016 23:10|
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