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What and Who
Title:Leveraging Unlabelled Corpora for Sentiment Analysis
Speaker:Kashyap Popat
coming from:Indian Institute of Technology Bombay
Speakers Bio:Graduate from Indian Institute of Technology Bombay
Event Type:PhD Application Talk
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Monday, 23 February 2015
Duration:120 Minutes
Building:E1 4
Expensive feature engineering based on WordNet senses has been shown to be useful for document level sentiment classification. A plausible reason for such a performance improvement is the reduction in data sparsity. However, such a reduction could be achieved with a lesser effort through the means of syntagma based word clustering which addresses the problem of data sparsity in sentiment analysis, both monolingual and cross-lingual. Experiments with Mono Lingual Sentiment Analysis (MLSA) show that cluster based data sparsity reduction leads to performance better than sense based classification for sentiment analysis at document level. Similar idea is applied to Cross Lingual Sentiment Analysis (CLSA), which shows that reduction in data sparsity (after translation or bilingual-mapping) produces accuracy higher than Machine Translation based CLSA and sense based CLSA.
Name(s):IMPRS-CS Office
Phone:0681 9325 1800
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Created:Stephanie Jörg/MPI-INF, 02/20/2015 10:06 AM Last modified:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Stephanie Jörg, 02/20/2015 10:26 AM
  • Stephanie Jörg, 02/20/2015 10:25 AM -- Created document.