Bringing Active Learning to Life
Ines Rehbein, Josef Ruppenhofer and Alexis Palmer
In: The 23rd International Conference on Computational Linguistics (COLING 2010), 23-27 Aug 2010, Beijing, China.
Active learning has been applied to different NLP tasks, with the aim of limiting the amount of time and cost for human annotation. Most studies on active learning have only simulated the annotation scenario, using relabelled gold standard data. We present the first active learning experiment for Word Sense Disambiguation with human annotators in a realistic environment, using fine-grained sense distinctions, and investigate whether AL can reduce annotation cost and boost classifier performance when applied to a real-world