@inproceedings{bar-haim-etal-2019-surrogacy,
title = "From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion",
author = "Bar-Haim, Roy and
Krieger, Dalia and
Toledo-Ronen, Orith and
Edelstein, Lilach and
Bilu, Yonatan and
Halfon, Alon and
Katz, Yoav and
Menczel, Amir and
Aharonov, Ranit and
Slonim, Noam",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1094",
doi = "10.18653/v1/P19-1094",
pages = "977--990",
abstract = "When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.",
}
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<abstract>When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.</abstract>
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%0 Conference Proceedings
%T From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion
%A Bar-Haim, Roy
%A Krieger, Dalia
%A Toledo-Ronen, Orith
%A Edelstein, Lilach
%A Bilu, Yonatan
%A Halfon, Alon
%A Katz, Yoav
%A Menczel, Amir
%A Aharonov, Ranit
%A Slonim, Noam
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 jul
%I Association for Computational Linguistics
%C Florence, Italy
%F bar-haim-etal-2019-surrogacy
%X When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.
%R 10.18653/v1/P19-1094
%U https://aclanthology.org/P19-1094
%U https://doi.org/10.18653/v1/P19-1094
%P 977-990
Markdown (Informal)
[From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion](https://aclanthology.org/P19-1094) (Bar-Haim et al., ACL 2019)
ACL
- Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, and Noam Slonim. 2019. From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 977–990, Florence, Italy. Association for Computational Linguistics.