@inproceedings{lignos-etal-2023-improving,
title = "Improving {NER} Research Workflows with {S}eq{S}core",
author = "Lignos, Constantine and
Kruse, Maya and
Rueda, Andrew",
editor = "Tan, Liling and
Milajevs, Dmitrijs and
Chauhan, Geeticka and
Gwinnup, Jeremy and
Rippeth, Elijah",
booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlposs-1.17",
doi = "10.18653/v1/2023.nlposs-1.17",
pages = "147--152",
abstract = "We describe the features of SeqScore, an MIT-licensed Python toolkit for working with named entity recognition (NER) data.While SeqScore began as a tool for NER scoring, it has been expanded to help with the full lifecycle of working with NER data: validating annotation, providing at-a-glance and detailed summaries of the data, modifying annotation to support experiments, scoring system output, and aiding with error analysis.SeqScore is released via PyPI (https://pypi.org/project/seqscore/) and development occurs on GitHub (https://github.com/bltlab/seqscore).",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lignos-etal-2023-improving">
<titleInfo>
<title>Improving NER Research Workflows with SeqScore</title>
</titleInfo>
<name type="personal">
<namePart type="given">Constantine</namePart>
<namePart type="family">Lignos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maya</namePart>
<namePart type="family">Kruse</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrew</namePart>
<namePart type="family">Rueda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Liling</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dmitrijs</namePart>
<namePart type="family">Milajevs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Geeticka</namePart>
<namePart type="family">Chauhan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jeremy</namePart>
<namePart type="family">Gwinnup</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elijah</namePart>
<namePart type="family">Rippeth</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Singapore</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We describe the features of SeqScore, an MIT-licensed Python toolkit for working with named entity recognition (NER) data.While SeqScore began as a tool for NER scoring, it has been expanded to help with the full lifecycle of working with NER data: validating annotation, providing at-a-glance and detailed summaries of the data, modifying annotation to support experiments, scoring system output, and aiding with error analysis.SeqScore is released via PyPI (https://pypi.org/project/seqscore/) and development occurs on GitHub (https://github.com/bltlab/seqscore).</abstract>
<identifier type="citekey">lignos-etal-2023-improving</identifier>
<identifier type="doi">10.18653/v1/2023.nlposs-1.17</identifier>
<location>
<url>https://aclanthology.org/2023.nlposs-1.17</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>147</start>
<end>152</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Improving NER Research Workflows with SeqScore
%A Lignos, Constantine
%A Kruse, Maya
%A Rueda, Andrew
%Y Tan, Liling
%Y Milajevs, Dmitrijs
%Y Chauhan, Geeticka
%Y Gwinnup, Jeremy
%Y Rippeth, Elijah
%S Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lignos-etal-2023-improving
%X We describe the features of SeqScore, an MIT-licensed Python toolkit for working with named entity recognition (NER) data.While SeqScore began as a tool for NER scoring, it has been expanded to help with the full lifecycle of working with NER data: validating annotation, providing at-a-glance and detailed summaries of the data, modifying annotation to support experiments, scoring system output, and aiding with error analysis.SeqScore is released via PyPI (https://pypi.org/project/seqscore/) and development occurs on GitHub (https://github.com/bltlab/seqscore).
%R 10.18653/v1/2023.nlposs-1.17
%U https://aclanthology.org/2023.nlposs-1.17
%U https://doi.org/10.18653/v1/2023.nlposs-1.17
%P 147-152
Markdown (Informal)
[Improving NER Research Workflows with SeqScore](https://aclanthology.org/2023.nlposs-1.17) (Lignos et al., NLPOSS-WS 2023)
ACL
- Constantine Lignos, Maya Kruse, and Andrew Rueda. 2023. Improving NER Research Workflows with SeqScore. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 147–152, Singapore. Association for Computational Linguistics.