The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Standard

The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab. / Hansen, Casper; Hansen, Christian; Simonsen, Jakob Grue; Lioma, Christina.

CLEF 2018 Working Notes. ed. / Linda Cappellato ; Nicola Ferro ; Jian-Yun Nie; Laure Soulier. 10. ed. CEUR-WS.org, 2018. 81 (CEUR Workshop Proceedings, Vol. 2125).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Harvard

Hansen, C, Hansen, C, Simonsen, JG & Lioma, C 2018, The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab. in L Cappellato , N Ferro , J-Y Nie & L Soulier (eds), CLEF 2018 Working Notes. 10 edn, 81, CEUR-WS.org, CEUR Workshop Proceedings, vol. 2125, 19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018, Avignon, France, 10/09/2018.

APA

Hansen, C., Hansen, C., Simonsen, J. G., & Lioma, C. (2018). The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab. In L. Cappellato , N. Ferro , J-Y. Nie, & L. Soulier (Eds.), CLEF 2018 Working Notes (10 ed.). [81] CEUR-WS.org. CEUR Workshop Proceedings Vol. 2125

Vancouver

Hansen C, Hansen C, Simonsen JG, Lioma C. The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab. In Cappellato L, Ferro N, Nie J-Y, Soulier L, editors, CLEF 2018 Working Notes. 10 ed. CEUR-WS.org. 2018. 81. (CEUR Workshop Proceedings, Vol. 2125).

Author

Hansen, Casper ; Hansen, Christian ; Simonsen, Jakob Grue ; Lioma, Christina. / The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab. CLEF 2018 Working Notes. editor / Linda Cappellato ; Nicola Ferro ; Jian-Yun Nie ; Laure Soulier. 10. ed. CEUR-WS.org, 2018. (CEUR Workshop Proceedings, Vol. 2125).

Bibtex

@inproceedings{069c4426de2a431dba28d3f3eaab606c,
title = "The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab",
abstract = "We predict which claim in a political debate should be prioritizedfor fact-checking. A particular challenge is, given a debate, how toproduce a ranked list of its sentences based on their worthiness for factchecking. We develop a Recurrent Neural Network (RNN) model thatlearns a sentence embedding, which is then used to predict the checkworthinessof a sentence. Our sentence embedding encodes both semanticand syntactic dependencies using pretrained word2vec word embeddingsas well as part-of-speech tagging and syntactic dependency parsing. Thisresults in a multi-representation of each word, which we use as input to aRNN with GRU memory units; the output from each word is aggregatedusing attention, followed by a fully connected layer, from which the outputis predicted using a sigmoid function. The overall performance of ourtechniques is successful, achieving the overall second best performing run(MAP: 0.1152) in the competition, as well as the highest overall performance(MAP: 0.1810) for our contrastive run with a 32% improvementover the second highest MAP score in the English language category. Inour primary run we combined our sentence embedding with state of theart check-worthy features, whereas in the contrastive run we consideredour sentence embedding alone",
keywords = "CNN, Fact checking, Political debates, RNN",
author = "Casper Hansen and Christian Hansen and Simonsen, {Jakob Grue} and Christina Lioma",
year = "2018",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS.org",
editor = "{Cappellato }, Linda and {Ferro }, Nicola and Nie, {Jian-Yun } and Laure Soulier",
booktitle = "CLEF 2018 Working Notes",
edition = "10",
note = "19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018 ; Conference date: 10-09-2018 Through 14-09-2018",

}

RIS

TY - GEN

T1 - The Copenhagen Team Participation in the Check-Worthiness Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the CLEF-2018 CheckThat! Lab

AU - Hansen, Casper

AU - Hansen, Christian

AU - Simonsen, Jakob Grue

AU - Lioma, Christina

PY - 2018

Y1 - 2018

N2 - We predict which claim in a political debate should be prioritizedfor fact-checking. A particular challenge is, given a debate, how toproduce a ranked list of its sentences based on their worthiness for factchecking. We develop a Recurrent Neural Network (RNN) model thatlearns a sentence embedding, which is then used to predict the checkworthinessof a sentence. Our sentence embedding encodes both semanticand syntactic dependencies using pretrained word2vec word embeddingsas well as part-of-speech tagging and syntactic dependency parsing. Thisresults in a multi-representation of each word, which we use as input to aRNN with GRU memory units; the output from each word is aggregatedusing attention, followed by a fully connected layer, from which the outputis predicted using a sigmoid function. The overall performance of ourtechniques is successful, achieving the overall second best performing run(MAP: 0.1152) in the competition, as well as the highest overall performance(MAP: 0.1810) for our contrastive run with a 32% improvementover the second highest MAP score in the English language category. Inour primary run we combined our sentence embedding with state of theart check-worthy features, whereas in the contrastive run we consideredour sentence embedding alone

AB - We predict which claim in a political debate should be prioritizedfor fact-checking. A particular challenge is, given a debate, how toproduce a ranked list of its sentences based on their worthiness for factchecking. We develop a Recurrent Neural Network (RNN) model thatlearns a sentence embedding, which is then used to predict the checkworthinessof a sentence. Our sentence embedding encodes both semanticand syntactic dependencies using pretrained word2vec word embeddingsas well as part-of-speech tagging and syntactic dependency parsing. Thisresults in a multi-representation of each word, which we use as input to aRNN with GRU memory units; the output from each word is aggregatedusing attention, followed by a fully connected layer, from which the outputis predicted using a sigmoid function. The overall performance of ourtechniques is successful, achieving the overall second best performing run(MAP: 0.1152) in the competition, as well as the highest overall performance(MAP: 0.1810) for our contrastive run with a 32% improvementover the second highest MAP score in the English language category. Inour primary run we combined our sentence embedding with state of theart check-worthy features, whereas in the contrastive run we consideredour sentence embedding alone

KW - CNN

KW - Fact checking

KW - Political debates

KW - RNN

M3 - Article in proceedings

T3 - CEUR Workshop Proceedings

BT - CLEF 2018 Working Notes

A2 - Cappellato , Linda

A2 - Ferro , Nicola

A2 - Nie, Jian-Yun

A2 - Soulier, Laure

PB - CEUR-WS.org

T2 - 19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018

Y2 - 10 September 2018 through 14 September 2018

ER -

ID: 202539747