@inproceedings{vo-popescu-2016-corpora,
title = "Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment",
author = "Vo, Ngoc Phuoc An and
Popescu, Octavian",
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/L16-1539",
pages = "3379--3386",
abstract = "In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgments and we proceed to the manual annotation with SR scores. The RTE 1-4 corpora used in the PASCAL competition fit our need. The annotators worked independently of one each other and they did not have access to the TE judgment during annotation. The intuition that the two annotations are correlated received major support from this experiment and this finding led to a system that uses this information to revise the initial estimates of SR scores. As semantic relatedness is one of the most general and difficult task in natural language processing we expect that future systems will combine different sources of information in order to solve it. Our work suggests that textual entailment plays a quantifiable role in addressing it.",
}
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%0 Conference Proceedings
%T Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment
%A Vo, Ngoc Phuoc An
%A Popescu, Octavian
%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 vo-popescu-2016-corpora
%X In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgments and we proceed to the manual annotation with SR scores. The RTE 1-4 corpora used in the PASCAL competition fit our need. The annotators worked independently of one each other and they did not have access to the TE judgment during annotation. The intuition that the two annotations are correlated received major support from this experiment and this finding led to a system that uses this information to revise the initial estimates of SR scores. As semantic relatedness is one of the most general and difficult task in natural language processing we expect that future systems will combine different sources of information in order to solve it. Our work suggests that textual entailment plays a quantifiable role in addressing it.
%U https://aclanthology.org/L16-1539
%P 3379-3386
Markdown (Informal)
[Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment](https://aclanthology.org/L16-1539) (Vo & Popescu, LREC 2016)
ACL