Abstract
Character-level Chinese named entity recognition
(NER) that applies long short-term memory
(LSTM) to incorporate lexicons has achieved
great success. However, this method fails to fully
exploit GPU parallelism and candidate lexicons
can conflict. In this work, we propose a faster
alternative to Chinese NER: a convolutional neural
network (CNN)-based method that incorporates
lexicons using a rethinking mechanism. The
proposed method can model all the characters and
potential words that match the sentence in parallel.
In addition, the rethinking mechanism can address
the word conflict by feeding back the high-level
features to refine the networks. Experimental
results on four datasets show that the proposed
method can achieve better performance than both
word-level and character-level baseline methods.
In addition, the proposed method performs up to
3.21 times faster than state-of-the-art methods,
while realizing better performance