Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/59128
Title: Key phrase extraction from user generated content
Authors: Lee, Angeline Kai Zhen
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2014
Abstract: [1] The Stanford Natural Language Processing Group. Retrieved from http://nlp.stanford.edu/ [2] L. Ratinov and D. Roth, Design Challenges and Misconceptions in Named Entity Recognition. CoNLL (2009) [3] OpenNLP. Retrieved from http://opennlp.apache.org/ [4] Stanford Log-linear Part-Of-Speech Tagger. Retrieved from http://nlp.stanford.edu/software/tagger.shtml [5] A. Turpin and W. Hersh. (2004). Do Clarity Scores for Queries Correlate with User Performance? [6] Steve Cronen-Townsend and W. Bruce Croft. (2002). Quantify Query Ambiguity [7] Kullback–Leibler divergence. Retrieved from http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence [8] Apache Lucene Core. Retrieved from http://lucene.apache.org/core/
URI: http://hdl.handle.net/10356/59128
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 SizeFormat 
Final Report - Amended.pdf
  Restricted Access
FYP Report on key phrase extraction1.75 MBAdobe PDFView/Open

Page view(s)

179
Updated on Nov 25, 2020

Download(s)

27
Updated on Nov 25, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.