期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：If we take an existing supervised NLP system,
a simple and general way to improve
accuracy is to use unsupervised word
representations as extra word features. We
evaluate Brown clusters, Collobert and
Weston (2008) embeddings, and HLBL
(Mnih & Hinton, 2009) embeddings
of words on both NER and chunking.
We use near state-of-the-art supervised
baselines, and find that each of the three
word representations improves the accuracy
of these baselines. We find further
improvements by combining different
word representations. You can download
our word features, for off-the-shelf use
in existing NLP systems, as well as our
code, here: http://metaoptimize.
com/projects/wordreprs/
