Title :
Joint Chinese word segmentation and punctuation prediction using deep recurrent neural network for social media data
Author :
Kui Wu; Xuancong Wang; Nina Zhou; AiTi Aw; Haizhou Li
Author_Institution :
Institute for Infocomm Research, Singapore
Abstract :
In this work, we propose to jointly perform Chinese word segmentation (CWS) and punctuation prediction (PU) in a unified framework using deep recurrent neural network (DRNN). We further perform a comparative study among the joint frameworks, the isolated prediction and the pipeline methods that link the two tasks sequentially, on a social media corpus. Our experimental results show that joint models improve performance of CWS and affect PU marginally. We also study the effects of CWS and PU on Chinese-to-English machine translation (MT) quality by evaluating on a parallel social media corpus. It is shown that joint models are superior to the isolated prediction and the pipeline approaches.
Keywords :
"Pipelines","Media","Artificial neural networks","Estimation"
Conference_Titel :
Asian Language Processing (IALP), 2015 International Conference on
Print_ISBN :
978-1-4673-9595-3
DOI :
10.1109/IALP.2015.7451527