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Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives.doi:10.24963/ijcai.2020/493 dblp:conf/ijcai/Zhu0T020 fatcat:ktpek73hm5dchmc6j246gdaj5q