@inproceedings{bosc-etal-2016-dart,
title = "{DART}: a Dataset of Arguments and their Relations on {T}witter",
author = "Bosc, Tom and
Cabrio, Elena and
Villata, Serena",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1200",
pages = "1258--1263",
abstract = "The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.",
}
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%0 Conference Proceedings
%T DART: a Dataset of Arguments and their Relations on Twitter
%A Bosc, Tom
%A Cabrio, Elena
%A Villata, Serena
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F bosc-etal-2016-dart
%X The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.
%U https://aclanthology.org/L16-1200
%P 1258-1263
Markdown (Informal)
[DART: a Dataset of Arguments and their Relations on Twitter](https://aclanthology.org/L16-1200) (Bosc et al., LREC 2016)
ACL
- Tom Bosc, Elena Cabrio, and Serena Villata. 2016. DART: a Dataset of Arguments and their Relations on Twitter. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1258–1263, Portorož, Slovenia. European Language Resources Association (ELRA).