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Active Learning for Detection of Stance Components

Skeppstedt, Maria; Sahlgren, Magnus; Paradis, Carita LU and Kerren, Andreas (2016) COLING 2016 In The 26th International Conference on Computational Linguistics p.50-59
Abstract
Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a... (More)
Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
active learning, stance, sentiment, annotation, classifier
in
The 26th International Conference on Computational Linguistics
pages
50 - 59
publisher
Association for Computational Linguistics
conference name
COLING 2016
ISBN
978-4-87974-723-5
language
English
LU publication?
yes
id
5e006d0b-f8b0-41be-bc0b-8551033e9643
date added to LUP
2016-11-28 20:30:05
date last changed
2017-11-14 09:54:37
@inproceedings{5e006d0b-f8b0-41be-bc0b-8551033e9643,
  abstract     = {Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition. },
  author       = {Skeppstedt, Maria and Sahlgren, Magnus and Paradis, Carita and Kerren, Andreas},
  booktitle    = {The 26th International Conference on Computational Linguistics},
  isbn         = {978-4-87974-723-5},
  keyword      = {active learning,stance,sentiment,annotation,classifier},
  language     = {eng},
  pages        = {50--59},
  publisher    = {Association for Computational Linguistics},
  title        = {Active Learning for Detection of Stance Components},
  year         = {2016},
}