Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/66649
Title: | English sentence unit detection based on text features | Authors: | Stephanie | Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2016 | Abstract: | Sentence unit detection in automated speech recognition (ASR) system is crucial for enriching the ASR output, improving the human readability, processing the word stream of the output and bridging the gap between the ASR system with the NLP applications.This thesis presents the machine learning models for sentence unit detection in written text. In this context, sentence unit (SUs) is referred to punctuation marks in the sentence, in which the focus is on adding period (“.”) to the unstructured word sequence. | URI: | http://hdl.handle.net/10356/66649 | Schools: | School of Computer Engineering | Research Centres: | Emerging Research Lab | Rights: | Nanyang Technological University | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FYP Report - Stephanie.pdf Restricted Access | Final Year Project Report | 2.44 MB | Adobe PDF | View/Open |
Page view(s)
417
Updated on Mar 28, 2024
Download(s) 50
31
Updated on Mar 28, 2024
Google ScholarTM
Check
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.