@article{sultan-etal-2014-back,
title = "Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence",
author = "Sultan, Md Arafat and
Bethard, Steven and
Sumner, Tamara",
editor = "Lin, Dekang and
Collins, Michael and
Lee, Lillian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "2",
year = "2014",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q14-1018",
doi = "10.1162/tacl_a_00178",
pages = "219--230",
abstract = "We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88{--}92{\%}, demonstrating 1{--}3{\%} absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.",
}
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<abstract>We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.</abstract>
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%0 Journal Article
%T Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
%A Sultan, Md Arafat
%A Bethard, Steven
%A Sumner, Tamara
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F sultan-etal-2014-back
%X We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.
%R 10.1162/tacl_a_00178
%U https://aclanthology.org/Q14-1018
%U https://doi.org/10.1162/tacl_a_00178
%P 219-230
Markdown (Informal)
[Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence](https://aclanthology.org/Q14-1018) (Sultan et al., TACL 2014)
ACL