  Although character-based models using lexicon have achieved promising results
for Chinese named entity recognition (NER) task, some lexical words would
introduce erroneous information due to wrongly matched words. Existing
researches proposed many strategies to integrate lexicon knowledge. However,
they performed with simple first-order lexicon knowledge, which provided
insufficient word information and still faced the challenge of matched word
boundary conflicts; or explored the lexicon knowledge with graph where
higher-order information introducing negative words may disturb the
identification. To alleviate the above limitations, we present new insight into
second-order lexicon knowledge (SLK) of each character in the sentence to
provide more lexical word information including semantic and word boundary
features. Based on these, we propose a SLK-based model with a novel strategy to
integrate the above lexicon knowledge. The proposed model can exploit more
discernible lexical words information with the help of global context.
Experimental results on three public datasets demonstrate the validity of SLK.
The proposed model achieves more excellent performance than the
state-of-the-art comparison methods.
