期刊名称:Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度:2011
卷号:2011
出版社:ACL Anthology
摘要:Active Learning (AL) is typically initialized
with a small seed of examples selected randomly.
However, when the distribution of
classes in the data is skewed, some classes
may be missed, resulting in a slow learning
progress. Our contribution is twofold: (1) we
show that an unsupervised language modeling
based technique is effective in selecting rare
class examples, and (2) we use this technique
for seeding AL and demonstrate that it leads
to a higher learning rate. The evaluation is
conducted in the context of word sense disambiguation.