@inproceedings{rijal-aryal-2025-howard,
title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval-Combining Zero-Shot Claim Extraction and {KNN}-Based Classification for Multilingual Claim Matching",
author = "Rijal, Suprabhat and
Aryal, Saurav",
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-1.234/",
pages = "1777--1782",
ISBN = "979-8-89176-273-2",
abstract = "SemEval Task 7 introduced a dataset for multilingual and cross-lingual fact checking. We propose a system that leverages similarity matching, KNN, zero-shot classification and summarization to retrieve fact-checks for social media posts across multiple languages. Our approach achieves performance within the expected range, aligning with baseline results. Although competitive, the findings highlight the potential and challenges of zero-shot methods, providing a foundation for future research in cross-lingual information verification."
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%0 Conference Proceedings
%T Howard University-AI4PC at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval-Combining Zero-Shot Claim Extraction and KNN-Based Classification for Multilingual Claim Matching
%A Rijal, Suprabhat
%A Aryal, Saurav
%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 rijal-aryal-2025-howard
%X SemEval Task 7 introduced a dataset for multilingual and cross-lingual fact checking. We propose a system that leverages similarity matching, KNN, zero-shot classification and summarization to retrieve fact-checks for social media posts across multiple languages. Our approach achieves performance within the expected range, aligning with baseline results. Although competitive, the findings highlight the potential and challenges of zero-shot methods, providing a foundation for future research in cross-lingual information verification.
%U https://aclanthology.org/2025.semeval-1.234/
%P 1777-1782
Markdown (Informal)
[Howard University-AI4PC at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval-Combining Zero-Shot Claim Extraction and KNN-Based Classification for Multilingual Claim Matching](https://aclanthology.org/2025.semeval-1.234/) (Rijal & Aryal, SemEval 2025)
ACL