@inproceedings{bestgen-2024-satlab,
title = "{SATL}ab at {S}em{E}val-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction",
author = "Bestgen, Yves",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.16/",
doi = "10.18653/v1/2024.semeval-1.16",
pages = "95--100",
abstract = "This paper presents the SATLab participation in SemEval 2024 Task 1 on Semantic Textual Relatedness. The proposed system predicts semantic relatedness by means of the Euclidean distance between the character ngram frequencies in the two sentences to evaluate. It employs no external resources, nor information from other instances present in the material. The system performs well, coming first in five of the twelve languages. However, there is little difference between the best systems."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bestgen-2024-satlab">
<titleInfo>
<title>SATLab at SemEval-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yves</namePart>
<namePart type="family">Bestgen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)</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">Harish</namePart>
<namePart type="family">Tayyar Madabushi</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">Sara</namePart>
<namePart type="family">Rosenthal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aiala</namePart>
<namePart type="family">Rosá</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper presents the SATLab participation in SemEval 2024 Task 1 on Semantic Textual Relatedness. The proposed system predicts semantic relatedness by means of the Euclidean distance between the character ngram frequencies in the two sentences to evaluate. It employs no external resources, nor information from other instances present in the material. The system performs well, coming first in five of the twelve languages. However, there is little difference between the best systems.</abstract>
<identifier type="citekey">bestgen-2024-satlab</identifier>
<identifier type="doi">10.18653/v1/2024.semeval-1.16</identifier>
<location>
<url>https://aclanthology.org/2024.semeval-1.16/</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>95</start>
<end>100</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SATLab at SemEval-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction
%A Bestgen, Yves
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F bestgen-2024-satlab
%X This paper presents the SATLab participation in SemEval 2024 Task 1 on Semantic Textual Relatedness. The proposed system predicts semantic relatedness by means of the Euclidean distance between the character ngram frequencies in the two sentences to evaluate. It employs no external resources, nor information from other instances present in the material. The system performs well, coming first in five of the twelve languages. However, there is little difference between the best systems.
%R 10.18653/v1/2024.semeval-1.16
%U https://aclanthology.org/2024.semeval-1.16/
%U https://doi.org/10.18653/v1/2024.semeval-1.16
%P 95-100
Markdown (Informal)
[SATLab at SemEval-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction](https://aclanthology.org/2024.semeval-1.16/) (Bestgen, SemEval 2024)
ACL