Paper: Punctuation Prediction with Transition-based Parsing

ACL ID P13-1074
Title Punctuation Prediction with Transition-based Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Punctuations are not available in automatic speech recognition outputs, which could cre- ate barriers to many subsequent text pro- cessing tasks. This paper proposes a novel method to predict punctuation symbols for the stream of words in transcribed speech texts. Our method jointly performs parsing and punctuation prediction by integrating a rich set of syntactic features when processing words from left to right. It can exploit a global view to capture long-range dependencies for punc- tuation prediction with linear complexity. The experimental results on the test data sets of IWSLT and TDT4 show that our method can achieve high-level performance in punctuation prediction over the stream of words in tran- scribed speech text.