Istituto di Scienza e Tecnologie dell'Informazione     
Marcheggiani D., Thierry A. An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences. In: EMNLP - Conference on Empirical Methods in Natural Language Processing (Doha, Qatar, 25-29 /10 2014). Proceedings, pp. 898 - 906. Association for Computational Linguistics, 2014.
Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most benefit in the creation of a classifier. AL allows to learn accurate systems with much less annotated data than what is required by pure supervised learning algorithms, hence limiting the tedious effort of annotating a large collection of data. We experimentally investigate the behavior of several AL strategies for sequence labeling tasks (in a partially-labeled scenario) tailored on Partially-Labeled Conditional Random Fields, on four sequence labeling tasks: phrase chunking, part-of-speech tagging, named-entity recognition, and bio-entity recognition.
URL: http://aclweb.org/anthology/D/D14/D14-1097.pdf
Subject semi supervised learning
sequence labeling
active learning
conditional random fields
68-XX Computer science For papers involving machine computations and programs in a specific mathematical area

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