@inproceedings{mikhalkova-karyakin-2017-punfields,
title = "{P}un{F}ields at {S}em{E}val-2017 Task 7: Employing {R}oget{'}s Thesaurus in Automatic Pun Recognition and Interpretation",
author = "Mikhalkova, Elena and
Karyakin, Yuri",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2072",
doi = "10.18653/v1/S17-2072",
pages = "426--431",
abstract = "The article describes a model of automatic interpretation of English puns, based on Roget{'}s Thesaurus, and its implementation, PunFields. In a pun, the algorithm discovers two groups of words that belong to two main semantic fields. The fields become a semantic vector based on which an SVM classifier learns to recognize puns. A rule-based model is then applied for recognition of intentionally ambiguous (target) words and their definitions. In SemEval Task 7 PunFields shows a considerably good result in pun classification, but requires improvement in searching for the target word and its definition.",
}
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<abstract>The article describes a model of automatic interpretation of English puns, based on Roget’s Thesaurus, and its implementation, PunFields. In a pun, the algorithm discovers two groups of words that belong to two main semantic fields. The fields become a semantic vector based on which an SVM classifier learns to recognize puns. A rule-based model is then applied for recognition of intentionally ambiguous (target) words and their definitions. In SemEval Task 7 PunFields shows a considerably good result in pun classification, but requires improvement in searching for the target word and its definition.</abstract>
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%0 Conference Proceedings
%T PunFields at SemEval-2017 Task 7: Employing Roget’s Thesaurus in Automatic Pun Recognition and Interpretation
%A Mikhalkova, Elena
%A Karyakin, Yuri
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F mikhalkova-karyakin-2017-punfields
%X The article describes a model of automatic interpretation of English puns, based on Roget’s Thesaurus, and its implementation, PunFields. In a pun, the algorithm discovers two groups of words that belong to two main semantic fields. The fields become a semantic vector based on which an SVM classifier learns to recognize puns. A rule-based model is then applied for recognition of intentionally ambiguous (target) words and their definitions. In SemEval Task 7 PunFields shows a considerably good result in pun classification, but requires improvement in searching for the target word and its definition.
%R 10.18653/v1/S17-2072
%U https://aclanthology.org/S17-2072
%U https://doi.org/10.18653/v1/S17-2072
%P 426-431
Markdown (Informal)
[PunFields at SemEval-2017 Task 7: Employing Roget’s Thesaurus in Automatic Pun Recognition and Interpretation](https://aclanthology.org/S17-2072) (Mikhalkova & Karyakin, SemEval 2017)
ACL