@inproceedings{shwartz-dagan-2018-paraphrase,
title = "Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations",
author = "Shwartz, Vered and
Dagan, Ido",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1111",
doi = "10.18653/v1/P18-1111",
pages = "1200--1211",
abstract = "Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.",
}
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<abstract>Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.</abstract>
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%0 Conference Proceedings
%T Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
%A Shwartz, Vered
%A Dagan, Ido
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F shwartz-dagan-2018-paraphrase
%X Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.
%R 10.18653/v1/P18-1111
%U https://aclanthology.org/P18-1111
%U https://doi.org/10.18653/v1/P18-1111
%P 1200-1211
Markdown (Informal)
[Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations](https://aclanthology.org/P18-1111) (Shwartz & Dagan, ACL 2018)
ACL