@inproceedings{issa-etal-2018-abstract,
title = "{A}bstract {M}eaning {R}epresentation for Paraphrase Detection",
author = "Issa, Fuad and
Damonte, Marco and
Cohen, Shay B. and
Yan, Xiaohui and
Chang, Yi",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1041",
doi = "10.18653/v1/N18-1041",
pages = "442--452",
abstract = {Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that na{\"\i}ve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6{\%} for accuracy and 90.0{\%} for F$_1$ measure.},
}
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<abstract>Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F₁ measure.</abstract>
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%0 Conference Proceedings
%T Abstract Meaning Representation for Paraphrase Detection
%A Issa, Fuad
%A Damonte, Marco
%A Cohen, Shay B.
%A Yan, Xiaohui
%A Chang, Yi
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F issa-etal-2018-abstract
%X Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F₁ measure.
%R 10.18653/v1/N18-1041
%U https://aclanthology.org/N18-1041
%U https://doi.org/10.18653/v1/N18-1041
%P 442-452
Markdown (Informal)
[Abstract Meaning Representation for Paraphrase Detection](https://aclanthology.org/N18-1041) (Issa et al., NAACL 2018)
ACL
- Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, and Yi Chang. 2018. Abstract Meaning Representation for Paraphrase Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 442–452, New Orleans, Louisiana. Association for Computational Linguistics.