@inproceedings{murrant-etal-2025-stfxnlp,
title = "{STFXNLP} at {S}em{E}val-2025 Task 11 Track A: Neural Network, Schema, and Next Word Prediction-based Approaches to Perceived Emotion Detection",
author = "Murrant, Noah and
Brooks, Samantha and
King, Milton",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.91/",
pages = "651--656",
ISBN = "979-8-89176-273-2",
abstract = "In this work, we discuss our models that were applied to the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Muhammad et al., 2025b). We focused on the English data set of track A, which involves determining what emotions the reader of a snippet of text is feeling. We applied three different types of models that vary in their approaches and reported our findings on the task{'}s test set. We found that the performance of our models differed from each other, but neither of our models outperformed the task{'}s baseline model."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="murrant-etal-2025-stfxnlp">
<titleInfo>
<title>STFXNLP at SemEval-2025 Task 11 Track A: Neural Network, Schema, and Next Word Prediction-based Approaches to Perceived Emotion Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Noah</namePart>
<namePart type="family">Murrant</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samantha</namePart>
<namePart type="family">Brooks</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Milton</namePart>
<namePart type="family">King</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
</titleInfo>
<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>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcos</namePart>
<namePart type="family">Zampieri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-273-2</identifier>
</relatedItem>
<abstract>In this work, we discuss our models that were applied to the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Muhammad et al., 2025b). We focused on the English data set of track A, which involves determining what emotions the reader of a snippet of text is feeling. We applied three different types of models that vary in their approaches and reported our findings on the task’s test set. We found that the performance of our models differed from each other, but neither of our models outperformed the task’s baseline model.</abstract>
<identifier type="citekey">murrant-etal-2025-stfxnlp</identifier>
<location>
<url>https://aclanthology.org/2025.semeval-1.91/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>651</start>
<end>656</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T STFXNLP at SemEval-2025 Task 11 Track A: Neural Network, Schema, and Next Word Prediction-based Approaches to Perceived Emotion Detection
%A Murrant, Noah
%A Brooks, Samantha
%A King, Milton
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F murrant-etal-2025-stfxnlp
%X In this work, we discuss our models that were applied to the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Muhammad et al., 2025b). We focused on the English data set of track A, which involves determining what emotions the reader of a snippet of text is feeling. We applied three different types of models that vary in their approaches and reported our findings on the task’s test set. We found that the performance of our models differed from each other, but neither of our models outperformed the task’s baseline model.
%U https://aclanthology.org/2025.semeval-1.91/
%P 651-656
Markdown (Informal)
[STFXNLP at SemEval-2025 Task 11 Track A: Neural Network, Schema, and Next Word Prediction-based Approaches to Perceived Emotion Detection](https://aclanthology.org/2025.semeval-1.91/) (Murrant et al., SemEval 2025)
ACL