Active Learning for Detection of Stance Components
Skeppstedt, Maria ; Sahlgren, Magnus ; Paradis, Carita LU
and Kerren, Andreas
(2016)
COLING 2016
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:
https://lup.lub.lu.se/record/5e006d0b-f8b0-41be-bc0b-8551033e9643
- author
- Skeppstedt, Maria
; Sahlgren, Magnus
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- active learning, stance, sentiment, annotation, classifier
- host publication
- The 26th International Conference on Computational Linguistics : Proceedings of COLING 2016 - Proceedings of COLING 2016
- pages
- 50 - 59
- publisher
- Association for Computational Linguistics
- conference name
- COLING 2016
- conference location
- Osaka, Japan
- conference dates
- 2016-12-11 - 2016-12-16
- ISBN
- 978-4-87974-723-5
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- 5e006d0b-f8b0-41be-bc0b-8551033e9643
- date added to LUP
- 2016-11-28 20:30:05
- date last changed
- 2019-03-08 02:29:10
@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 : Proceedings of COLING 2016}},
isbn = {{978-4-87974-723-5}},
keywords = {{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}},
}