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Named entity recognition is a challenging task in Natural LanguageProcessing, especially for informal and noisy social media text. Chinese wordboundaries are also entity boundaries, therefore, named entity recognition forChinese text can benefit from word boundary detection, outputted by Chineseword segmentation. Yet Chinese word segmentation poses its own difficultybecause it is influenced by several factors, e.g., segmentation criteria,employed algorithm, etc. Dealt improperly, it may generate a cascading failureto the quality of named entity recognition followed. In this paper we integratea boundary assembling method with the state-of-the-art deep neural networkmodel, and incorporate the updated word boundary information into a conditionalrandom field model for named entity recognition. Our method shows a 2% absoluteimprovement over previous state-of-the-art results.

Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text