1 research outputs found
Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text
Named entity recognition is a challenging task in Natural Language
Processing, especially for informal and noisy social media text. Chinese word
boundaries are also entity boundaries, therefore, named entity recognition for
Chinese text can benefit from word boundary detection, outputted by Chinese
word segmentation. Yet Chinese word segmentation poses its own difficulty
because it is influenced by several factors, e.g., segmentation criteria,
employed algorithm, etc. Dealt improperly, it may generate a cascading failure
to the quality of named entity recognition followed. In this paper we integrate
a boundary assembling method with the state-of-the-art deep neural network
model, and incorporate the updated word boundary information into a conditional
random field model for named entity recognition. Our method shows a 2% absolute
improvement over previous state-of-the-art results