@inproceedings{kohli-tiwari-2023-arguably,
title = "Arguably at {S}em{E}val-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models",
author = "Kohli, Guneet Singh and
Tiwari, Vinayak",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.295",
doi = "10.18653/v1/2023.semeval-1.295",
pages = "2137--2142",
abstract = "We describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on the annotators to model them independently and combine the results to yield soft and hard scores. Our team focused on experimenting with hierarchical clustering with various distance metrics for similarity, dissimilarity, and reliability. We modeled the clusters and assigned weightage to find the soft and hard scores. Our team was able to find out hidden behavioral patterns among the judgments of annotators after rigorous experiments. The proposed system is made available.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kohli-tiwari-2023-arguably">
<titleInfo>
<title>Arguably at SemEval-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Guneet</namePart>
<namePart type="given">Singh</namePart>
<namePart type="family">Kohli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vinayak</namePart>
<namePart type="family">Tiwari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Sartori</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on the annotators to model them independently and combine the results to yield soft and hard scores. Our team focused on experimenting with hierarchical clustering with various distance metrics for similarity, dissimilarity, and reliability. We modeled the clusters and assigned weightage to find the soft and hard scores. Our team was able to find out hidden behavioral patterns among the judgments of annotators after rigorous experiments. The proposed system is made available.</abstract>
<identifier type="citekey">kohli-tiwari-2023-arguably</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.295</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.295</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>2137</start>
<end>2142</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Arguably at SemEval-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models
%A Kohli, Guneet Singh
%A Tiwari, Vinayak
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kohli-tiwari-2023-arguably
%X We describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on the annotators to model them independently and combine the results to yield soft and hard scores. Our team focused on experimenting with hierarchical clustering with various distance metrics for similarity, dissimilarity, and reliability. We modeled the clusters and assigned weightage to find the soft and hard scores. Our team was able to find out hidden behavioral patterns among the judgments of annotators after rigorous experiments. The proposed system is made available.
%R 10.18653/v1/2023.semeval-1.295
%U https://aclanthology.org/2023.semeval-1.295
%U https://doi.org/10.18653/v1/2023.semeval-1.295
%P 2137-2142
Markdown (Informal)
[Arguably at SemEval-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models](https://aclanthology.org/2023.semeval-1.295) (Kohli & Tiwari, SemEval 2023)
ACL