---
_id: '3820'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Giovanni
full_name: Da San Martino, Giovanni
last_name: Da San Martino
- first_name: Dora
full_name: Kiesel, Dora
last_name: Kiesel
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Wachsmuth H, Da San Martino G, Kiesel D, Stein B. The Impact of Modeling Overall
Argumentation with Tree Kernels. In: Proceedings of the 2017 Conference on
Empirical Methods in Natural Language Processing. ; 2017:2369-2379.'
apa: Wachsmuth, H., Da San Martino, G., Kiesel, D., & Stein, B. (2017). The
Impact of Modeling Overall Argumentation with Tree Kernels. In Proceedings
of the 2017 Conference on Empirical Methods in Natural Language Processing
(pp. 2369–2379).
bibtex: '@inproceedings{Wachsmuth_Da San Martino_Kiesel_Stein_2017, title={The Impact
of Modeling Overall Argumentation with Tree Kernels}, booktitle={Proceedings of
the 2017 Conference on Empirical Methods in Natural Language Processing}, author={Wachsmuth,
Henning and Da San Martino, Giovanni and Kiesel, Dora and Stein, Benno}, year={2017},
pages={2369–2379} }'
chicago: Wachsmuth, Henning, Giovanni Da San Martino, Dora Kiesel, and Benno Stein.
“The Impact of Modeling Overall Argumentation with Tree Kernels.” In Proceedings
of the 2017 Conference on Empirical Methods in Natural Language Processing,
2369–79, 2017.
ieee: H. Wachsmuth, G. Da San Martino, D. Kiesel, and B. Stein, “The Impact of Modeling
Overall Argumentation with Tree Kernels,” in Proceedings of the 2017 Conference
on Empirical Methods in Natural Language Processing, 2017, pp. 2369–2379.
mla: Wachsmuth, Henning, et al. “The Impact of Modeling Overall Argumentation with
Tree Kernels.” Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017, pp. 2369–79.
short: 'H. Wachsmuth, G. Da San Martino, D. Kiesel, B. Stein, in: Proceedings of
the 2017 Conference on Empirical Methods in Natural Language Processing, 2017,
pp. 2369–2379.'
date_created: 2018-08-02T13:38:48Z
date_updated: 2022-01-06T06:59:37Z
department:
- _id: '568'
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/D17-1253.pdf
page: 2369-2379
publication: Proceedings of the 2017 Conference on Empirical Methods in Natural Language
Processing
status: public
title: The Impact of Modeling Overall Argumentation with Tree Kernels
type: conference
user_id: '82920'
year: '2017'
...
---
_id: '3881'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Nona
full_name: Naderi, Nona
last_name: Naderi
- first_name: Yufang
full_name: Hou, Yufang
last_name: Hou
- first_name: Yonatan
full_name: Bilu, Yonatan
last_name: Bilu
- first_name: Vinodkumar
full_name: Prabhakaran, Vinodkumar
last_name: Prabhakaran
- first_name: Tim Alberdingk
full_name: Thijm, Tim Alberdingk
last_name: Thijm
- first_name: Graeme
full_name: Hirst, Graeme
last_name: Hirst
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Wachsmuth H, Naderi N, Hou Y, et al. Computational Argumentation Quality Assessment
in Natural Language. In: Proceedings of the 15th Conference of the European
Chapter of the Association for Computational Linguistics: Volume 1, Long Papers.
; 2017:176-187.'
apa: 'Wachsmuth, H., Naderi, N., Hou, Y., Bilu, Y., Prabhakaran, V., Thijm, T. A.,
… Stein, B. (2017). Computational Argumentation Quality Assessment in Natural
Language. In Proceedings of the 15th Conference of the European Chapter of
the Association for Computational Linguistics: Volume 1, Long Papers (pp.
176–187).'
bibtex: '@inproceedings{Wachsmuth_Naderi_Hou_Bilu_Prabhakaran_Thijm_Hirst_Stein_2017,
title={Computational Argumentation Quality Assessment in Natural Language}, booktitle={Proceedings
of the 15th Conference of the European Chapter of the Association for Computational
Linguistics: Volume 1, Long Papers}, author={Wachsmuth, Henning and Naderi, Nona
and Hou, Yufang and Bilu, Yonatan and Prabhakaran, Vinodkumar and Thijm, Tim Alberdingk
and Hirst, Graeme and Stein, Benno}, year={2017}, pages={176–187} }'
chicago: 'Wachsmuth, Henning, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar
Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, and Benno Stein. “Computational
Argumentation Quality Assessment in Natural Language.” In Proceedings of the
15th Conference of the European Chapter of the Association for Computational Linguistics:
Volume 1, Long Papers, 176–87, 2017.'
ieee: 'H. Wachsmuth et al., “Computational Argumentation Quality Assessment
in Natural Language,” in Proceedings of the 15th Conference of the European
Chapter of the Association for Computational Linguistics: Volume 1, Long Papers,
2017, pp. 176–187.'
mla: 'Wachsmuth, Henning, et al. “Computational Argumentation Quality Assessment
in Natural Language.” Proceedings of the 15th Conference of the European Chapter
of the Association for Computational Linguistics: Volume 1, Long Papers, 2017,
pp. 176–87.'
short: 'H. Wachsmuth, N. Naderi, Y. Hou, Y. Bilu, V. Prabhakaran, T.A. Thijm, G.
Hirst, B. Stein, in: Proceedings of the 15th Conference of the European Chapter
of the Association for Computational Linguistics: Volume 1, Long Papers, 2017,
pp. 176–187.'
date_created: 2018-08-11T16:17:16Z
date_updated: 2022-01-06T06:59:47Z
department:
- _id: '600'
- _id: '568'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/E17-1017.pdf
page: 176-187
publication: 'Proceedings of the 15th Conference of the European Chapter of the Association
for Computational Linguistics: Volume 1, Long Papers'
status: public
title: Computational Argumentation Quality Assessment in Natural Language
type: conference
user_id: '82920'
year: '2017'
...
---
_id: '3882'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Wachsmuth H, Stein B. A Universal Model for Discourse-Level Argumentation
Analysis. Special Section of the ACM Transactions on Internet Technology: Argumentation
in Social Media. 2017;(3):1-24.'
apa: 'Wachsmuth, H., & Stein, B. (2017). A Universal Model for Discourse-Level
Argumentation Analysis. Special Section of the ACM Transactions on Internet
Technology: Argumentation in Social Media, (3), 1–24.'
bibtex: '@article{Wachsmuth_Stein_2017, title={A Universal Model for Discourse-Level
Argumentation Analysis}, number={3}, journal={Special Section of the ACM Transactions
on Internet Technology: Argumentation in Social Media}, author={Wachsmuth, Henning
and Stein, Benno}, year={2017}, pages={1–24} }'
chicago: 'Wachsmuth, Henning, and Benno Stein. “A Universal Model for Discourse-Level
Argumentation Analysis.” Special Section of the ACM Transactions on Internet
Technology: Argumentation in Social Media, no. 3 (2017): 1–24.'
ieee: 'H. Wachsmuth and B. Stein, “A Universal Model for Discourse-Level Argumentation
Analysis,” Special Section of the ACM Transactions on Internet Technology:
Argumentation in Social Media, no. 3, pp. 1–24, 2017.'
mla: 'Wachsmuth, Henning, and Benno Stein. “A Universal Model for Discourse-Level
Argumentation Analysis.” Special Section of the ACM Transactions on Internet
Technology: Argumentation in Social Media, no. 3, 2017, pp. 1–24.'
short: 'H. Wachsmuth, B. Stein, Special Section of the ACM Transactions on Internet
Technology: Argumentation in Social Media (2017) 1–24.'
date_created: 2018-08-11T16:18:03Z
date_updated: 2022-01-06T06:59:47Z
department:
- _id: '568'
- _id: '600'
issue: '3'
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/pdf/10.1145/2957757
page: 1-24
publication: 'Special Section of the ACM Transactions on Internet Technology: Argumentation
in Social Media'
publication_identifier:
issn:
- 1533-5399
status: public
title: A Universal Model for Discourse-Level Argumentation Analysis
type: journal_article
user_id: '82920'
year: '2017'
...
---
_id: '3883'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Nona
full_name: Naderi, Nona
last_name: Naderi
- first_name: Ivan
full_name: Habernal, Ivan
last_name: Habernal
- first_name: Yufang
full_name: Hou, Yufang
last_name: Hou
- first_name: Graeme
full_name: Hirst, Graeme
last_name: Hirst
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Wachsmuth H, Naderi N, Habernal I, et al. Argumentation Quality Assessment:
Theory vs. Practice. In: Proceedings of the 55th Annual Meeting of the Association
for Computational Linguistics (Volume 2: Short Papers). ; 2017:250-255. doi:10.18653/v1/P17-2039'
apa: 'Wachsmuth, H., Naderi, N., Habernal, I., Hou, Y., Hirst, G., Gurevych, I.,
& Stein, B. (2017). Argumentation Quality Assessment: Theory vs. Practice.
In Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers) (pp. 250–255). https://doi.org/10.18653/v1/P17-2039'
bibtex: '@inproceedings{Wachsmuth_Naderi_Habernal_Hou_Hirst_Gurevych_Stein_2017,
title={Argumentation Quality Assessment: Theory vs. Practice}, DOI={10.18653/v1/P17-2039},
booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers)}, author={Wachsmuth, Henning and Naderi,
Nona and Habernal, Ivan and Hou, Yufang and Hirst, Graeme and Gurevych, Iryna
and Stein, Benno}, year={2017}, pages={250–255} }'
chicago: 'Wachsmuth, Henning, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst,
Iryna Gurevych, and Benno Stein. “Argumentation Quality Assessment: Theory vs.
Practice.” In Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (Volume 2: Short Papers), 250–55, 2017. https://doi.org/10.18653/v1/P17-2039.'
ieee: 'H. Wachsmuth et al., “Argumentation Quality Assessment: Theory vs.
Practice,” in Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (Volume 2: Short Papers), 2017, pp. 250–255.'
mla: 'Wachsmuth, Henning, et al. “Argumentation Quality Assessment: Theory vs. Practice.”
Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers), 2017, pp. 250–55, doi:10.18653/v1/P17-2039.'
short: 'H. Wachsmuth, N. Naderi, I. Habernal, Y. Hou, G. Hirst, I. Gurevych, B.
Stein, in: Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers), 2017, pp. 250–255.'
date_created: 2018-08-11T16:18:38Z
date_updated: 2022-01-06T06:59:47Z
department:
- _id: '568'
- _id: '600'
doi: 10.18653/v1/P17-2039
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/P17-2039.pdf
page: 250-255
publication: 'Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers)'
status: public
title: 'Argumentation Quality Assessment: Theory vs. Practice'
type: conference
user_id: '82920'
year: '2017'
...
---
_id: '3904'
author:
- first_name: Matthias
full_name: Hagen, Matthias
last_name: Hagen
- first_name: Johannes
full_name: Kiesel, Johannes
last_name: Kiesel
- first_name: Milad
full_name: Alshomary, Milad
id: '73059'
last_name: Alshomary
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Hagen M, Kiesel J, Alshomary M, Stein B. Webis at the CLEF 2017 Dynamic Search
Lab. In: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation
Forum. ; 2017.'
apa: Hagen, M., Kiesel, J., Alshomary, M., & Stein, B. (2017). Webis at the
CLEF 2017 Dynamic Search Lab. In Working Notes of CLEF 2017 - Conference and
Labs of the Evaluation Forum.
bibtex: '@inproceedings{Hagen_Kiesel_Alshomary_Stein_2017, title={Webis at the CLEF
2017 Dynamic Search Lab}, booktitle={Working Notes of CLEF 2017 - Conference and
Labs of the Evaluation Forum}, author={Hagen, Matthias and Kiesel, Johannes and
Alshomary, Milad and Stein, Benno}, year={2017} }'
chicago: Hagen, Matthias, Johannes Kiesel, Milad Alshomary, and Benno Stein. “Webis
at the CLEF 2017 Dynamic Search Lab.” In Working Notes of CLEF 2017 - Conference
and Labs of the Evaluation Forum, 2017.
ieee: M. Hagen, J. Kiesel, M. Alshomary, and B. Stein, “Webis at the CLEF 2017 Dynamic
Search Lab,” in Working Notes of CLEF 2017 - Conference and Labs of the Evaluation
Forum, 2017.
mla: Hagen, Matthias, et al. “Webis at the CLEF 2017 Dynamic Search Lab.” Working
Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, 2017.
short: 'M. Hagen, J. Kiesel, M. Alshomary, B. Stein, in: Working Notes of CLEF 2017
- Conference and Labs of the Evaluation Forum, 2017.'
date_created: 2018-08-14T13:28:44Z
date_updated: 2022-01-06T06:59:54Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: http://ceur-ws.org/Vol-1866/paper_198.pdf
publication: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum
status: public
title: Webis at the CLEF 2017 Dynamic Search Lab
type: conference
user_id: '73059'
year: '2017'
...
---
_id: '14884'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Yi-Pei
full_name: Chen, Yi-Pei
last_name: Chen
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
citation:
ama: 'Chen W-F, Chen Y-P, Ku L-W. How to Get Endorsements? Predicting Facebook Likes
Using Post Content and User Engagement. In: International Conference on HCI
in Business, Government, and Organizations. ; 2017:190-202.'
apa: Chen, W.-F., Chen, Y.-P., & Ku, L.-W. (2017). How to Get Endorsements?
Predicting Facebook Likes Using Post Content and User Engagement. In International
Conference on HCI in Business, Government, and Organizations (pp. 190–202).
bibtex: '@inproceedings{Chen_Chen_Ku_2017, title={How to Get Endorsements? Predicting
Facebook Likes Using Post Content and User Engagement}, booktitle={International
Conference on HCI in Business, Government, and Organizations}, author={Chen, Wei-Fan
and Chen, Yi-Pei and Ku, Lun-Wei}, year={2017}, pages={190–202} }'
chicago: Chen, Wei-Fan, Yi-Pei Chen, and Lun-Wei Ku. “How to Get Endorsements? Predicting
Facebook Likes Using Post Content and User Engagement.” In International Conference
on HCI in Business, Government, and Organizations, 190–202, 2017.
ieee: W.-F. Chen, Y.-P. Chen, and L.-W. Ku, “How to Get Endorsements? Predicting
Facebook Likes Using Post Content and User Engagement,” in International Conference
on HCI in Business, Government, and Organizations, 2017, pp. 190–202.
mla: Chen, Wei-Fan, et al. “How to Get Endorsements? Predicting Facebook Likes Using
Post Content and User Engagement.” International Conference on HCI in Business,
Government, and Organizations, 2017, pp. 190–202.
short: 'W.-F. Chen, Y.-P. Chen, L.-W. Ku, in: International Conference on HCI in
Business, Government, and Organizations, 2017, pp. 190–202.'
date_created: 2019-11-11T12:31:01Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://www.iis.sinica.edu.tw/papers/lwku/20654-F.pdf
page: 190-202
publication: International Conference on HCI in Business, Government, and Organizations
status: public
title: How to Get Endorsements? Predicting Facebook Likes Using Post Content and User
Engagement
type: conference
user_id: '82920'
year: '2017'
...
---
_id: '3747'
author:
- first_name: Khalid
full_name: Al Khatib, Khalid
last_name: Al Khatib
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Johannes
full_name: Kiesel, Johannes
last_name: Kiesel
- first_name: Matthias
full_name: Hagen, Matthias
last_name: Hagen
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Al Khatib K, Wachsmuth H, Kiesel J, Hagen M, Stein B. A News Editorial Corpus
for Mining Argumentation Strategies. In: Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics: Technical Papers. ;
2016:3433-3443.'
apa: 'Al Khatib, K., Wachsmuth, H., Kiesel, J., Hagen, M., & Stein, B. (2016).
A News Editorial Corpus for Mining Argumentation Strategies. In Proceedings
of COLING 2016, the 26th International Conference on Computational Linguistics:
Technical Papers (pp. 3433–3443).'
bibtex: '@inproceedings{Al Khatib_Wachsmuth_Kiesel_Hagen_Stein_2016, title={A News
Editorial Corpus for Mining Argumentation Strategies}, booktitle={Proceedings
of COLING 2016, the 26th International Conference on Computational Linguistics:
Technical Papers}, author={Al Khatib, Khalid and Wachsmuth, Henning and Kiesel,
Johannes and Hagen, Matthias and Stein, Benno}, year={2016}, pages={3433–3443}
}'
chicago: 'Al Khatib, Khalid, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen,
and Benno Stein. “A News Editorial Corpus for Mining Argumentation Strategies.”
In Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: Technical Papers, 3433–43, 2016.'
ieee: 'K. Al Khatib, H. Wachsmuth, J. Kiesel, M. Hagen, and B. Stein, “A News Editorial
Corpus for Mining Argumentation Strategies,” in Proceedings of COLING 2016,
the 26th International Conference on Computational Linguistics: Technical Papers,
2016, pp. 3433–3443.'
mla: 'Al Khatib, Khalid, et al. “A News Editorial Corpus for Mining Argumentation
Strategies.” Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: Technical Papers, 2016, pp. 3433–43.'
short: 'K. Al Khatib, H. Wachsmuth, J. Kiesel, M. Hagen, B. Stein, in: Proceedings
of COLING 2016, the 26th International Conference on Computational Linguistics:
Technical Papers, 2016, pp. 3433–3443.'
date_created: 2018-08-02T11:32:06Z
date_updated: 2022-01-06T06:59:34Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/C16-1324.pdf
page: 3433-3443
publication: 'Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: Technical Papers'
status: public
title: A News Editorial Corpus for Mining Argumentation Strategies
type: conference
user_id: '82920'
year: '2016'
...
---
_id: '3801'
author:
- first_name: Khalid
full_name: Al-Khatib, Khalid
last_name: Al-Khatib
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Matthias
full_name: Hagen, Matthias
last_name: Hagen
- first_name: Jonas
full_name: Köhler, Jonas
last_name: Köhler
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Al-Khatib K, Wachsmuth H, Hagen M, Köhler J, Stein B. Cross-Domain Mining
of Argumentative Text through Distant Supervision. In: Proceedings of the 2016
Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies. ; 2016:1395-1404. doi:10.18653/v1/N16-1165'
apa: 'Al-Khatib, K., Wachsmuth, H., Hagen, M., Köhler, J., & Stein, B. (2016).
Cross-Domain Mining of Argumentative Text through Distant Supervision. In Proceedings
of the 2016 Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies (pp. 1395–1404). https://doi.org/10.18653/v1/N16-1165'
bibtex: '@inproceedings{Al-Khatib_Wachsmuth_Hagen_Köhler_Stein_2016, title={Cross-Domain
Mining of Argumentative Text through Distant Supervision}, DOI={10.18653/v1/N16-1165},
booktitle={Proceedings of the 2016 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies}, author={Al-Khatib,
Khalid and Wachsmuth, Henning and Hagen, Matthias and Köhler, Jonas and Stein,
Benno}, year={2016}, pages={1395–1404} }'
chicago: 'Al-Khatib, Khalid, Henning Wachsmuth, Matthias Hagen, Jonas Köhler, and
Benno Stein. “Cross-Domain Mining of Argumentative Text through Distant Supervision.”
In Proceedings of the 2016 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language Technologies, 1395–1404,
2016. https://doi.org/10.18653/v1/N16-1165.'
ieee: 'K. Al-Khatib, H. Wachsmuth, M. Hagen, J. Köhler, and B. Stein, “Cross-Domain
Mining of Argumentative Text through Distant Supervision,” in Proceedings of
the 2016 Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, 2016, pp. 1395–1404.'
mla: 'Al-Khatib, Khalid, et al. “Cross-Domain Mining of Argumentative Text through
Distant Supervision.” Proceedings of the 2016 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language Technologies,
2016, pp. 1395–404, doi:10.18653/v1/N16-1165.'
short: 'K. Al-Khatib, H. Wachsmuth, M. Hagen, J. Köhler, B. Stein, in: Proceedings
of the 2016 Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, 2016, pp. 1395–1404.'
date_created: 2018-08-02T13:38:23Z
date_updated: 2022-01-06T06:59:36Z
department:
- _id: '600'
doi: 10.18653/v1/N16-1165
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/N16-1165.pdf
page: 1395-1404
publication: 'Proceedings of the 2016 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies'
status: public
title: Cross-Domain Mining of Argumentative Text through Distant Supervision
type: conference
user_id: '82920'
year: '2016'
...
---
_id: '3816'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Khalid
full_name: Al Khatib, Khalid
last_name: Al Khatib
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Wachsmuth H, Al Khatib K, Stein B. Using Argument Mining to Assess the Argumentation
Quality of Essays. In: Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: Technical Papers. ; 2016:1680-1691.'
apa: 'Wachsmuth, H., Al Khatib, K., & Stein, B. (2016). Using Argument Mining
to Assess the Argumentation Quality of Essays. In Proceedings of COLING 2016,
the 26th International Conference on Computational Linguistics: Technical Papers
(pp. 1680–1691).'
bibtex: '@inproceedings{Wachsmuth_Al Khatib_Stein_2016, title={Using Argument Mining
to Assess the Argumentation Quality of Essays}, booktitle={Proceedings of COLING
2016, the 26th International Conference on Computational Linguistics: Technical
Papers}, author={Wachsmuth, Henning and Al Khatib, Khalid and Stein, Benno}, year={2016},
pages={1680–1691} }'
chicago: 'Wachsmuth, Henning, Khalid Al Khatib, and Benno Stein. “Using Argument
Mining to Assess the Argumentation Quality of Essays.” In Proceedings of COLING
2016, the 26th International Conference on Computational Linguistics: Technical
Papers, 1680–91, 2016.'
ieee: 'H. Wachsmuth, K. Al Khatib, and B. Stein, “Using Argument Mining to Assess
the Argumentation Quality of Essays,” in Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics: Technical Papers, 2016,
pp. 1680–1691.'
mla: 'Wachsmuth, Henning, et al. “Using Argument Mining to Assess the Argumentation
Quality of Essays.” Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: Technical Papers, 2016, pp. 1680–91.'
short: 'H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of COLING 2016, the
26th International Conference on Computational Linguistics: Technical Papers,
2016, pp. 1680–1691.'
date_created: 2018-08-02T13:38:43Z
date_updated: 2022-01-06T06:59:37Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/C16-1158.pdf
page: 1680-1691
publication: 'Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: Technical Papers'
publication_identifier:
isbn:
- 978-3-88579-975-7
status: public
title: Using Argument Mining to Assess the Argumentation Quality of Essays
type: conference
user_id: '82920'
year: '2016'
...
---
_id: '3880'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Wachsmuth H. Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse. In:
Ausgezeichnete Informatikdissertationen 2015. ; 2016:329-338.'
apa: Wachsmuth, H. (2016). Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse.
In Ausgezeichnete Informatikdissertationen 2015 (pp. 329–338).
bibtex: '@inproceedings{Wachsmuth_2016, title={Pipelines Für Effiziente und Robuste
Ad-hoc Textanalyse}, booktitle={Ausgezeichnete Informatikdissertationen 2015},
author={Wachsmuth, Henning}, year={2016}, pages={329–338} }'
chicago: Wachsmuth, Henning. “Pipelines Für Effiziente Und Robuste Ad-Hoc Textanalyse.”
In Ausgezeichnete Informatikdissertationen 2015, 329–38, 2016.
ieee: H. Wachsmuth, “Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse,” in
Ausgezeichnete Informatikdissertationen 2015, 2016, pp. 329–338.
mla: Wachsmuth, Henning. “Pipelines Für Effiziente Und Robuste Ad-Hoc Textanalyse.”
Ausgezeichnete Informatikdissertationen 2015, 2016, pp. 329–38.
short: 'H. Wachsmuth, in: Ausgezeichnete Informatikdissertationen 2015, 2016, pp.
329–338.'
date_created: 2018-08-11T16:16:00Z
date_updated: 2022-01-06T06:59:47Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://dl.gi.de/bitstream/handle/20.500.12116/4595/329.pdf?sequence=1
page: 329-338
publication: Ausgezeichnete Informatikdissertationen 2015
publication_identifier:
isbn:
- 978-3-88579-975-7
status: public
title: Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse
type: conference
user_id: '82920'
year: '2016'
...
---
_id: '14881'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
citation:
ama: 'Chen W-F, Ku L-W. UTCNN: a Deep Learning Model of Stance Classification on
Social Media Text. In: Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics. ; 2016:1635-1645.'
apa: 'Chen, W.-F., & Ku, L.-W. (2016). UTCNN: a Deep Learning Model of Stance
Classification on Social Media Text. In Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics (pp. 1635–1645).'
bibtex: '@inproceedings{Chen_Ku_2016, title={UTCNN: a Deep Learning Model of Stance
Classification on Social Media Text}, booktitle={Proceedings of COLING 2016, the
26th International Conference on Computational Linguistics}, author={Chen, Wei-Fan
and Ku, Lun-Wei}, year={2016}, pages={1635–1645} }'
chicago: 'Chen, Wei-Fan, and Lun-Wei Ku. “UTCNN: A Deep Learning Model of Stance
Classification on Social Media Text.” In Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics, 1635–45, 2016.'
ieee: 'W.-F. Chen and L.-W. Ku, “UTCNN: a Deep Learning Model of Stance Classification
on Social Media Text,” in Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics, 2016, pp. 1635–1645.'
mla: 'Chen, Wei-Fan, and Lun-Wei Ku. “UTCNN: A Deep Learning Model of Stance Classification
on Social Media Text.” Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics, 2016, pp. 1635–45.'
short: 'W.-F. Chen, L.-W. Ku, in: Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics, 2016, pp. 1635–1645.'
date_created: 2019-11-11T12:28:24Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/C16-1154.pdf
page: 1635-1645
publication: Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics
status: public
title: 'UTCNN: a Deep Learning Model of Stance Classification on Social Media Text'
type: conference
user_id: '82920'
year: '2016'
...
---
_id: '14882'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Fang-Yu
full_name: Lin, Fang-Yu
last_name: Lin
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
citation:
ama: 'Chen W-F, Lin F-Y, Ku L-W. WordForce: Visualizing Controversial Words in Debates.
In: Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: System Demonstrations. ; 2016:273-277.'
apa: 'Chen, W.-F., Lin, F.-Y., & Ku, L.-W. (2016). WordForce: Visualizing Controversial
Words in Debates. In Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: System Demonstrations (pp. 273–277).'
bibtex: '@inproceedings{Chen_Lin_Ku_2016, title={WordForce: Visualizing Controversial
Words in Debates}, booktitle={Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics: System Demonstrations}, author={Chen,
Wei-Fan and Lin, Fang-Yu and Ku, Lun-Wei}, year={2016}, pages={273–277} }'
chicago: 'Chen, Wei-Fan, Fang-Yu Lin, and Lun-Wei Ku. “WordForce: Visualizing Controversial
Words in Debates.” In Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: System Demonstrations, 273–77, 2016.'
ieee: 'W.-F. Chen, F.-Y. Lin, and L.-W. Ku, “WordForce: Visualizing Controversial
Words in Debates,” in Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: System Demonstrations, 2016, pp. 273–277.'
mla: 'Chen, Wei-Fan, et al. “WordForce: Visualizing Controversial Words in Debates.”
Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: System Demonstrations, 2016, pp. 273–77.'
short: 'W.-F. Chen, F.-Y. Lin, L.-W. Ku, in: Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics: System Demonstrations,
2016, pp. 273–277.'
date_created: 2019-11-11T12:29:14Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/C16-2057.pdf
page: 273-277
publication: 'Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: System Demonstrations'
status: public
title: 'WordForce: Visualizing Controversial Words in Debates'
type: conference
user_id: '82920'
year: '2016'
...
---
_id: '14883'
author:
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
citation:
ama: 'Ku L-W, Chen W-F. Chinese Textual Sentiment Analysis: Datasets, Resources
and Tools. In: Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: Tutorial Abstracts. ; 2016:5-8.'
apa: 'Ku, L.-W., & Chen, W.-F. (2016). Chinese Textual Sentiment Analysis: Datasets,
Resources and Tools. In Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics: Tutorial Abstracts (pp. 5–8).'
bibtex: '@inproceedings{Ku_Chen_2016, title={Chinese Textual Sentiment Analysis:
Datasets, Resources and Tools}, booktitle={Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics: Tutorial Abstracts}, author={Ku,
Lun-Wei and Chen, Wei-Fan}, year={2016}, pages={5–8} }'
chicago: 'Ku, Lun-Wei, and Wei-Fan Chen. “Chinese Textual Sentiment Analysis: Datasets,
Resources and Tools.” In Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics: Tutorial Abstracts, 5–8, 2016.'
ieee: 'L.-W. Ku and W.-F. Chen, “Chinese Textual Sentiment Analysis: Datasets, Resources
and Tools,” in Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: Tutorial Abstracts, 2016, pp. 5–8.'
mla: 'Ku, Lun-Wei, and Wei-Fan Chen. “Chinese Textual Sentiment Analysis: Datasets,
Resources and Tools.” Proceedings of COLING 2016, the 26th International Conference
on Computational Linguistics: Tutorial Abstracts, 2016, pp. 5–8.'
short: 'L.-W. Ku, W.-F. Chen, in: Proceedings of COLING 2016, the 26th International
Conference on Computational Linguistics: Tutorial Abstracts, 2016, pp. 5–8.'
date_created: 2019-11-11T12:30:04Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/C16-3002.pdf
page: 5-8
publication: 'Proceedings of COLING 2016, the 26th International Conference on Computational
Linguistics: Tutorial Abstracts'
status: public
title: 'Chinese Textual Sentiment Analysis: Datasets, Resources and Tools'
type: conference_abstract
user_id: '82920'
year: '2016'
...
---
_id: '3815'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Johannes
full_name: Kiesel, Johannes
last_name: Kiesel
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Wachsmuth H, Kiesel J, Stein B. Sentiment Flow - A General Model of Web Review
Argumentation. In: Tsujii J, Hajic J, eds. Proceedings of the 2015 Conference
on Empirical Methods in Natural Language Processing. Lecture Notes in Computer
Science. ; 2015:601-611. doi:10.18653/v1/D15-1072'
apa: Wachsmuth, H., Kiesel, J., & Stein, B. (2015). Sentiment Flow - A General
Model of Web Review Argumentation. In J. Tsujii & J. Hajic (Eds.), Proceedings
of the 2015 Conference on Empirical Methods in Natural Language Processing
(pp. 601–611). https://doi.org/10.18653/v1/D15-1072
bibtex: '@inproceedings{Wachsmuth_Kiesel_Stein_2015, series={Lecture Notes in Computer
Science}, title={Sentiment Flow - A General Model of Web Review Argumentation},
DOI={10.18653/v1/D15-1072},
booktitle={Proceedings of the 2015 Conference on Empirical Methods in Natural
Language Processing}, author={Wachsmuth, Henning and Kiesel, Johannes and Stein,
Benno}, editor={Tsujii, Junichi and Hajic, JanEditors}, year={2015}, pages={601–611},
collection={Lecture Notes in Computer Science} }'
chicago: Wachsmuth, Henning, Johannes Kiesel, and Benno Stein. “Sentiment Flow -
A General Model of Web Review Argumentation.” In Proceedings of the 2015 Conference
on Empirical Methods in Natural Language Processing, edited by Junichi Tsujii
and Jan Hajic, 601–11. Lecture Notes in Computer Science, 2015. https://doi.org/10.18653/v1/D15-1072.
ieee: H. Wachsmuth, J. Kiesel, and B. Stein, “Sentiment Flow - A General Model of
Web Review Argumentation,” in Proceedings of the 2015 Conference on Empirical
Methods in Natural Language Processing, 2015, pp. 601–611.
mla: Wachsmuth, Henning, et al. “Sentiment Flow - A General Model of Web Review
Argumentation.” Proceedings of the 2015 Conference on Empirical Methods in
Natural Language Processing, edited by Junichi Tsujii and Jan Hajic, 2015,
pp. 601–11, doi:10.18653/v1/D15-1072.
short: 'H. Wachsmuth, J. Kiesel, B. Stein, in: J. Tsujii, J. Hajic (Eds.), Proceedings
of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015,
pp. 601–611.'
date_created: 2018-08-02T13:38:41Z
date_updated: 2022-01-06T06:59:37Z
department:
- _id: '600'
doi: 10.18653/v1/D15-1072
editor:
- first_name: Junichi
full_name: Tsujii, Junichi
last_name: Tsujii
- first_name: Jan
full_name: Hajic, Jan
last_name: Hajic
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/D15-1072.pdf
page: 601-611
publication: Proceedings of the 2015 Conference on Empirical Methods in Natural Language
Processing
publication_identifier:
isbn:
- 978-3-319-25740-2
series_title: Lecture Notes in Computer Science
status: public
title: Sentiment Flow - A General Model of Web Review Argumentation
type: conference
user_id: '82920'
year: '2015'
...
---
_id: '3879'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: Wachsmuth H. Text Analysis Pipelines - Towards Ad-Hoc Large-Scale Text Mining.;
2015. doi:http://dx.doi.org/10.1007/978-3-319-25741-9
apa: Wachsmuth, H. (2015). Text Analysis Pipelines - Towards Ad-hoc Large-scale
Text Mining. http://dx.doi.org/10.1007/978-3-319-25741-9
bibtex: '@book{Wachsmuth_2015, series={Lecture Notes in Computer Science}, title={Text
Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining}, DOI={http://dx.doi.org/10.1007/978-3-319-25741-9},
author={Wachsmuth, Henning}, year={2015}, collection={Lecture Notes in Computer
Science} }'
chicago: Wachsmuth, Henning. Text Analysis Pipelines - Towards Ad-Hoc Large-Scale
Text Mining. Lecture Notes in Computer Science, 2015. http://dx.doi.org/10.1007/978-3-319-25741-9.
ieee: H. Wachsmuth, Text Analysis Pipelines - Towards Ad-hoc Large-scale Text
Mining. 2015.
mla: Wachsmuth, Henning. Text Analysis Pipelines - Towards Ad-Hoc Large-Scale
Text Mining. 2015, doi:http://dx.doi.org/10.1007/978-3-319-25741-9.
short: H. Wachsmuth, Text Analysis Pipelines - Towards Ad-Hoc Large-Scale Text Mining,
2015.
date_created: 2018-08-11T16:15:08Z
date_updated: 2022-01-06T06:59:47Z
department:
- _id: '600'
doi: http://dx.doi.org/10.1007/978-3-319-25741-9
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-319-25740-2
series_title: Lecture Notes in Computer Science
status: public
title: Text Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining
type: book
user_id: '82920'
year: '2015'
...
---
_id: '7568'
abstract:
- lang: eng
text: 'Today''s web search and big data analytics applications aim to address information
needs~(typically given in the form of search queries) ad-hoc on large numbers
of texts. In order to directly return relevant information instead of only returning
potentially relevant texts, these applications have begun to employ text mining.
The term text mining covers tasks that deal with the inference of structured high-quality
information from collections and streams of unstructured input texts. Text mining
requires task-specific text analysis processes that may consist of several interdependent
steps. These processes are realized with sequences of algorithms from information
extraction, text classification, and natural language processing. However, the
use of such text analysis pipelines is still restricted to addressing a few predefined
information needs. We argue that the reasons behind are three-fold: First, text
analysis pipelines are usually made manually in respect of the given information
need and input texts, because their design requires expert knowledge about the
algorithms to be employed. When information needs have to be addressed that are
unknown beforehand, text mining hence cannot be performed ad-hoc. Second, text
analysis pipelines tend to be inefficient in terms of run-time, because their
execution often includes analyzing texts with computationally expensive algorithms.
When information needs have to be addressed ad-hoc, text mining hence cannot be
performed in the large. And third, text analysis pipelines tend not to robustly
achieve high effectiveness on all texts, because their results are often inferred
by algorithms that rely on domain-dependent features of texts. Hence, text mining
currently cannot guarantee to infer high-quality information. In this thesis,
we contribute to the question of how to address information needs from text mining
ad-hoc in an efficient and domain-robust manner. We observe that knowledge about
a text analysis process and information obtained within the process help to improve
the design, the execution, and the results of the pipeline that realizes the process.
To this end, we apply different techniques from classical and statistical artificial
intelligence. In particular, we first develop knowledge-based approaches for an
ad-hoc pipeline construction and for an optimal execution of a pipeline on its
input. Then, we show theoretically and practically how to optimize and adapt the
schedule of the algorithms in a pipeline based on information in the analyzed
input texts in order to maximize execution efficiency. Finally, we learn patterns
in the argumentation structures of texts statistically that remain strongly invariant
across domains and that, thereby, allow for more robust analysis results in a
restricted set of tasks. We formally analyze all developed approaches and we implement
them as open-source software applications. Based on these applications, we evaluate
the approaches on established and on newly created collections of texts for scientifically
and industrially important text analysis tasks, such as financial event extraction
and fine-grained sentiment analysis. Our findings show that text analysis pipelines
can be designed automatically, which process only portions of text that are relevant
for the information need at hand. Through scheduling, the run-time efficiency
of pipelines can be improved by up to more than one order of magnitude while maintaining
effectiveness. Moreover, we provide evidence that a pipeline''s domain robustness
substantially benefits from focusing on argumentation structure in tasks like
sentiment analysis. We conclude that our approaches denote essential building
blocks of enabling ad-hoc large-scale text mining in web search and big data analytics
applications.'
author:
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: Wachsmuth H. Pipelines for Ad-Hoc Large-Scale Text Mining.; 2015.
apa: Wachsmuth, H. (2015). Pipelines for Ad-hoc Large-scale Text Mining.
bibtex: '@book{Wachsmuth_2015, title={Pipelines for Ad-hoc Large-scale Text Mining},
author={Wachsmuth, Henning}, year={2015} }'
chicago: Wachsmuth, Henning. Pipelines for Ad-Hoc Large-Scale Text Mining,
2015.
ieee: H. Wachsmuth, Pipelines for Ad-hoc Large-scale Text Mining. 2015.
mla: Wachsmuth, Henning. Pipelines for Ad-Hoc Large-Scale Text Mining. 2015.
short: H. Wachsmuth, Pipelines for Ad-Hoc Large-Scale Text Mining, 2015.
date_created: 2019-02-06T14:14:29Z
date_updated: 2022-01-06T07:03:39Z
department:
- _id: '66'
- _id: '600'
language:
- iso: eng
related_material:
link:
- relation: confirmation
url: https://webis.de/downloads/publications/papers/wachsmuth_2013.pdf
status: public
title: Pipelines for Ad-hoc Large-scale Text Mining
type: dissertation
user_id: '82920'
year: '2015'
...
---
_id: '14875'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: MeiHua
full_name: Chen, MeiHua
last_name: Chen
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
citation:
ama: 'Chen W-F, Chen M, Ku L-W. Embarrassed or Awkward? Ranking Emotion Synonyms
for ESL Learners’ Appropriate Wording. In: Proceedings of the Tenth Workshop
on Innovative Use of NLP for Building Educational Applications. ; 2015:144-153.'
apa: Chen, W.-F., Chen, M., & Ku, L.-W. (2015). Embarrassed or Awkward? Ranking
Emotion Synonyms for ESL Learners’ Appropriate Wording. In Proceedings of the
Tenth Workshop on Innovative Use of NLP for Building Educational Applications
(pp. 144–153).
bibtex: '@inproceedings{Chen_Chen_Ku_2015, title={Embarrassed or Awkward? Ranking
Emotion Synonyms for ESL Learners’ Appropriate Wording}, booktitle={Proceedings
of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications},
author={Chen, Wei-Fan and Chen, MeiHua and Ku, Lun-Wei}, year={2015}, pages={144–153}
}'
chicago: Chen, Wei-Fan, MeiHua Chen, and Lun-Wei Ku. “Embarrassed or Awkward? Ranking
Emotion Synonyms for ESL Learners’ Appropriate Wording.” In Proceedings of
the Tenth Workshop on Innovative Use of NLP for Building Educational Applications,
144–53, 2015.
ieee: W.-F. Chen, M. Chen, and L.-W. Ku, “Embarrassed or Awkward? Ranking Emotion
Synonyms for ESL Learners’ Appropriate Wording,” in Proceedings of the Tenth
Workshop on Innovative Use of NLP for Building Educational Applications, 2015,
pp. 144–153.
mla: Chen, Wei-Fan, et al. “Embarrassed or Awkward? Ranking Emotion Synonyms for
ESL Learners’ Appropriate Wording.” Proceedings of the Tenth Workshop on Innovative
Use of NLP for Building Educational Applications, 2015, pp. 144–53.
short: 'W.-F. Chen, M. Chen, L.-W. Ku, in: Proceedings of the Tenth Workshop on
Innovative Use of NLP for Building Educational Applications, 2015, pp. 144–153.'
date_created: 2019-11-11T12:21:53Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: https://www.aclweb.org/anthology/W15-0617.pdf
page: 144-153
publication: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building
Educational Applications
status: public
title: Embarrassed or Awkward? Ranking Emotion Synonyms for ESL Learners’ Appropriate
Wording
type: conference
user_id: '82920'
year: '2015'
...
---
_id: '14877'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
- first_name: Yann-Hui
full_name: Lee, Yann-Hui
last_name: Lee
citation:
ama: 'Chen W-F, Ku L-W, Lee Y-H. Mining Supportive and Unsupportive Evidence from
Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example. In:
2015 AAAI Spring Symposium Series. ; 2015.'
apa: Chen, W.-F., Ku, L.-W., & Lee, Y.-H. (2015). Mining Supportive and Unsupportive
Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as
an Example. In 2015 AAAI Spring Symposium Series.
bibtex: '@inproceedings{Chen_Ku_Lee_2015, title={Mining Supportive and Unsupportive
Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as
an Example}, booktitle={2015 AAAI Spring Symposium Series}, author={Chen, Wei-Fan
and Ku, Lun-Wei and Lee, Yann-Hui}, year={2015} }'
chicago: Chen, Wei-Fan, Lun-Wei Ku, and Yann-Hui Lee. “Mining Supportive and Unsupportive
Evidence from Facebook Using Anti-Reconstruction of the Nuclear Power Plant as
an Example.” In 2015 AAAI Spring Symposium Series, 2015.
ieee: W.-F. Chen, L.-W. Ku, and Y.-H. Lee, “Mining Supportive and Unsupportive Evidence
from Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example,”
in 2015 AAAI Spring Symposium Series, 2015.
mla: Chen, Wei-Fan, et al. “Mining Supportive and Unsupportive Evidence from Facebook
Using Anti-Reconstruction of the Nuclear Power Plant as an Example.” 2015 AAAI
Spring Symposium Series, 2015.
short: 'W.-F. Chen, L.-W. Ku, Y.-H. Lee, in: 2015 AAAI Spring Symposium Series,
2015.'
date_created: 2019-11-11T12:24:50Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://www.aaai.org/ocs/index.php/SSS/SSS15/paper/viewFile/10270/10093
publication: 2015 AAAI Spring Symposium Series
status: public
title: Mining Supportive and Unsupportive Evidence from Facebook Using Anti-reconstruction
of the Nuclear Power Plant as an Example
type: conference
user_id: '82920'
year: '2015'
...
---
_id: '14878'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Yann-Hui
full_name: Lee, Yann-Hui
last_name: Lee
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
citation:
ama: 'Chen W-F, Lee Y-H, Ku L-W. Topic-based Stance Mining for Social Media Texts.
In: International Conference on HCI in Business. ; 2015:22-33.'
apa: Chen, W.-F., Lee, Y.-H., & Ku, L.-W. (2015). Topic-based Stance Mining
for Social Media Texts. In International Conference on HCI in Business
(pp. 22–33).
bibtex: '@inproceedings{Chen_Lee_Ku_2015, title={Topic-based Stance Mining for Social
Media Texts}, booktitle={International Conference on HCI in Business}, author={Chen,
Wei-Fan and Lee, Yann-Hui and Ku, Lun-Wei}, year={2015}, pages={22–33} }'
chicago: Chen, Wei-Fan, Yann-Hui Lee, and Lun-Wei Ku. “Topic-Based Stance Mining
for Social Media Texts.” In International Conference on HCI in Business,
22–33, 2015.
ieee: W.-F. Chen, Y.-H. Lee, and L.-W. Ku, “Topic-based Stance Mining for Social
Media Texts,” in International Conference on HCI in Business, 2015, pp.
22–33.
mla: Chen, Wei-Fan, et al. “Topic-Based Stance Mining for Social Media Texts.” International
Conference on HCI in Business, 2015, pp. 22–33.
short: 'W.-F. Chen, Y.-H. Lee, L.-W. Ku, in: International Conference on HCI in
Business, 2015, pp. 22–33.'
date_created: 2019-11-11T12:25:52Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
language:
- iso: eng
main_file_link:
- url: http://www.iis.sinica.edu.tw/papers/lwku/18917-F.pdf
page: 22-33
publication: International Conference on HCI in Business
status: public
title: Topic-based Stance Mining for Social Media Texts
type: conference
user_id: '82920'
year: '2015'
...
---
_id: '14879'
author:
- first_name: Wei-Fan
full_name: Chen, Wei-Fan
id: '82920'
last_name: Chen
- first_name: Mei-Hua
full_name: Chen, Mei-Hua
last_name: Chen
- first_name: Ming-Lung
full_name: Chen, Ming-Lung
last_name: Chen
- first_name: Lun-Wei
full_name: Ku, Lun-Wei
last_name: Ku
citation:
ama: Chen W-F, Chen M-H, Chen M-L, Ku L-W. A Computer-assistance Learning System
for Emotional Wording. IEEE Transactions on Knowledge and Data Engineering.
2015;28(5):1093-1104.
apa: Chen, W.-F., Chen, M.-H., Chen, M.-L., & Ku, L.-W. (2015). A Computer-assistance
Learning System for Emotional Wording. IEEE Transactions on Knowledge and Data
Engineering, 28(5), 1093–1104.
bibtex: '@article{Chen_Chen_Chen_Ku_2015, title={A Computer-assistance Learning
System for Emotional Wording}, volume={28}, number={5}, journal={IEEE Transactions
on Knowledge and Data Engineering}, publisher={IEEE}, author={Chen, Wei-Fan and
Chen, Mei-Hua and Chen, Ming-Lung and Ku, Lun-Wei}, year={2015}, pages={1093–1104}
}'
chicago: 'Chen, Wei-Fan, Mei-Hua Chen, Ming-Lung Chen, and Lun-Wei Ku. “A Computer-Assistance
Learning System for Emotional Wording.” IEEE Transactions on Knowledge and
Data Engineering 28, no. 5 (2015): 1093–1104.'
ieee: W.-F. Chen, M.-H. Chen, M.-L. Chen, and L.-W. Ku, “A Computer-assistance Learning
System for Emotional Wording,” IEEE Transactions on Knowledge and Data Engineering,
vol. 28, no. 5, pp. 1093–1104, 2015.
mla: Chen, Wei-Fan, et al. “A Computer-Assistance Learning System for Emotional
Wording.” IEEE Transactions on Knowledge and Data Engineering, vol. 28,
no. 5, IEEE, 2015, pp. 1093–104.
short: W.-F. Chen, M.-H. Chen, M.-L. Chen, L.-W. Ku, IEEE Transactions on Knowledge
and Data Engineering 28 (2015) 1093–1104.
date_created: 2019-11-11T12:27:00Z
date_updated: 2022-01-06T06:52:09Z
department:
- _id: '600'
intvolume: ' 28'
issue: '5'
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/iel7/69/4358933/07355346.pdf
page: 1093-1104
publication: IEEE Transactions on Knowledge and Data Engineering
publisher: IEEE
status: public
title: A Computer-assistance Learning System for Emotional Wording
type: journal_article
user_id: '82920'
volume: 28
year: '2015'
...