Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/120143
Title: THESIS TITLE: ADVANCES IN PUNCTUATION AND DISFLUENCY PREDICTION
Authors: WANG XUANCONG
Keywords: punctuation, disfluency, prediction, machine, learning
Issue Date: 23-Jan-2015
Source: WANG XUANCONG (2015-01-23). THESIS TITLE: ADVANCES IN PUNCTUATION AND DISFLUENCY PREDICTION. ScholarBank@NUS Repository.
Abstract: This thesis improves state-of-the-art natural language processing (NLP) techniques in automatic punctuation and disfluency prediction. It can be applied to post-process automatically recognized speech output for downstream NLP tasks. For punctuation prediction, we propose using dynamic conditional random fields for joint sentence boundary and punctuation prediction. We have also investigated several model optimization techniques which are important for practical applications. For disfluency prediction, we propose a beam-search decoder approach. Our decoder can combine generative models like n-gram language models (LM) and discriminative models like Max-margin Markov Networks (M3N). Lastly, we have performed an empirical study on various state-of-the-art methods for combining the two tasks, and we have highlighted some insights in balancing the trade-off between performance and efficiency for building practical systems.
URI: http://scholarbank.nus.edu.sg/handle/10635/120143
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
WangXC.pdf1.81 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

111
checked on Jan 20, 2018

Download(s)

102
checked on Jan 20, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.