  Word embeddings -- distributed word representations that can be learned from
unlabelled data -- have been shown to have high utility in many natural
language processing applications. In this paper, we perform an extrinsic
evaluation of five popular word embedding methods in the context of four
sequence labelling tasks: POS-tagging, syntactic chunking, NER and MWE
identification. A particular focus of the paper is analysing the effects of
task-based updating of word representations. We show that when using word
embeddings as features, as few as several hundred training instances are
sufficient to achieve competitive results, and that word embeddings lead to
improvements over OOV words and out of domain. Perhaps more surprisingly, our
results indicate there is little difference between the different word
embedding methods, and that simple Brown clusters are often competitive with
word embeddings across all tasks we consider.
