@inproceedings{nguyen-etal-2017-distinguishing,
title = "Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network",
author = "Nguyen, Kim Anh and
Schulte im Walde, Sabine and
Vu, Ngoc Thang",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1008",
pages = "76--85",
abstract = "Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods.",
}
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%0 Conference Proceedings
%T Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
%A Nguyen, Kim Anh
%A Schulte im Walde, Sabine
%A Vu, Ngoc Thang
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F nguyen-etal-2017-distinguishing
%X Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods.
%U https://aclanthology.org/E17-1008
%P 76-85
Markdown (Informal)
[Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network](https://aclanthology.org/E17-1008) (Nguyen et al., EACL 2017)
ACL