Abstract
Annotations on structured corpora provide a foundational instrument for emerging linguistic research. To generate annotations automatically, data-driven dependency parsers need a large annotated corpus to learn from. But these annotations are expensive to collect and require a labor intensive task. In order to reduce the costs of annotation, we provide a novel framework in which a committee of dependency parsers collaborate to improve their efficiency using active learning.
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Majidi, S., Crane, G. (2013). Committee-Based Active Learning for Dependency Parsing. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2013. Lecture Notes in Computer Science, vol 8092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40501-3_56
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DOI: https://doi.org/10.1007/978-3-642-40501-3_56
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