@inproceedings{yang-etal-2021-unsupervised,
title = "Unsupervised Paradigm Clustering Using Transformation Rules",
author = "Yang, Changbing and
Nicolai, Garrett and
Silfverberg, Miikka",
editor = "Nicolai, Garrett and
Gorman, Kyle and
Cotterell, Ryan",
booktitle = "Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigmorphon-1.11",
doi = "10.18653/v1/2021.sigmorphon-1.11",
pages = "98--106",
abstract = "This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering. Our system generates paradigms using morphological transformation rules which are discovered from raw data. We experiment with two methods for discovering rules. Our first approach generates prefix and suffix transformations between similar strings. Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations. We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-etal-2021-unsupervised">
<titleInfo>
<title>Unsupervised Paradigm Clustering Using Transformation Rules</title>
</titleInfo>
<name type="personal">
<namePart type="given">Changbing</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Garrett</namePart>
<namePart type="family">Nicolai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Miikka</namePart>
<namePart type="family">Silfverberg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
</titleInfo>
<name type="personal">
<namePart type="given">Garrett</namePart>
<namePart type="family">Nicolai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kyle</namePart>
<namePart type="family">Gorman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ryan</namePart>
<namePart type="family">Cotterell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering. Our system generates paradigms using morphological transformation rules which are discovered from raw data. We experiment with two methods for discovering rules. Our first approach generates prefix and suffix transformations between similar strings. Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations. We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.</abstract>
<identifier type="citekey">yang-etal-2021-unsupervised</identifier>
<identifier type="doi">10.18653/v1/2021.sigmorphon-1.11</identifier>
<location>
<url>https://aclanthology.org/2021.sigmorphon-1.11</url>
</location>
<part>
<date>2021-08</date>
<extent unit="page">
<start>98</start>
<end>106</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Unsupervised Paradigm Clustering Using Transformation Rules
%A Yang, Changbing
%A Nicolai, Garrett
%A Silfverberg, Miikka
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yang-etal-2021-unsupervised
%X This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering. Our system generates paradigms using morphological transformation rules which are discovered from raw data. We experiment with two methods for discovering rules. Our first approach generates prefix and suffix transformations between similar strings. Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations. We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.
%R 10.18653/v1/2021.sigmorphon-1.11
%U https://aclanthology.org/2021.sigmorphon-1.11
%U https://doi.org/10.18653/v1/2021.sigmorphon-1.11
%P 98-106
Markdown (Informal)
[Unsupervised Paradigm Clustering Using Transformation Rules](https://aclanthology.org/2021.sigmorphon-1.11) (Yang et al., SIGMORPHON 2021)
ACL
- Changbing Yang, Garrett Nicolai, and Miikka Silfverberg. 2021. Unsupervised Paradigm Clustering Using Transformation Rules. In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 98–106, Online. Association for Computational Linguistics.