Subject Headings: Active Annotation
- Active learning; Machine learning; Named entity recognition; Natural language processing; Information extraction; Corpus annotation
The annotation of corpora has become a crucial prerequisite for information extraction systems which heavily rely on supervised machine learning techniques and therefore require large amounts of annotated training material. Annotation, however, requires human intervention and is thus an extremely costly, labor-intensive, and error-prone process. The burden of annotation is one of the major obstacles when well-established information extraction systems are to be applied to real-world problems and so a pressing research question is how annotation can be made more efficient. Most annotated corpora are built by collecting the documents to be annotated on a random sampling basis or based on simple keyword search. Only recently, more sophisticated approaches to select the base material in order to reduce annotation effort are being investigated. One promising direction is known as Active Learning (AL) where only examples of high utility for classifier training are selected for manual annotation. Because of this intelligent selection, classifiers of a certain target performance can be yieled with less labeled data points. This thesis centers around the question how AL can be applied as resource-aware strategy for linguistic annotation. A set of requirements is defined and several approaches and adaptations to the standard form of AL are proposed to meet these requirements. This includes: (1) a novel method to monitor and stop the AL-driven annotation process; (2) an approach to semi-supervised AL where only highly critical tokens have to actually be manually annotated while the rest is automatically tagged; (3) a discussion and empirical investigation of the reusability of actively drawn samples; (4) a comparative study how class imbalance can be reduced right upfront during AL-driven data acquisition; (5) two methods for selective sampling of examples which are useful for multiple learning problems; (6) an extensive evaluation of the proposed approaches to AL for Named Entity Recognition with respect to both savings in corpus size and actual annotation time; and finally (7) three methods how these approaches can be made cost-conscious so as to reduce annotation time even more.
|2010 ResourceAwareAnnotationthroughA||Katrin Tomanek||Resource-aware Annotation through Active Learning||2010|