Papers by Majid Zarharan
Reference-Free Evaluation of Taxonomies (2026.findings-acl)
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| Challenge: | Taxonomies are used to classify items, ideas or organisms based on shared characteristics. |
| Approach: | They introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. |
| Outcome: | The proposed metrics correlate well with F1 against ground truth taxonomies on five taxonomies and improve hierarchical classification when used with label hierarchies. |
FarExStance: Explainable Stance Detection for Farsi (2025.coling-main)
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Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh, Sauleh Eetemadi, Mohammad Taher Pilehvar, Jennifer Foster
| Challenge: | FarExStance is a new dataset for explainable stance detection in Farsi . it contains extractive explanations as evidence for stance labels and claims . |
| Approach: | They propose a dataset for explainable stance detection in Farsi with extractive explanations as evidence. |
| Outcome: | The proposed model is the most accurate on stance detection, while the best explanation is from few-shot Claude-3.5-Sonnet. |
ParsFEVER: a Dataset for Farsi Fact Extraction and Verification (2021.starsem-1)
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Majid Zarharan, Mahsa Ghaderan, Amin Pourdabiri, Zahra Sayedi, Behrouz Minaei-Bidgoli, Sauleh Eetemadi, Mohammad Taher Pilehvar
| Challenge: | Existing methods for fact-checking and verification require large amounts of annotated data, but this is limited to low-resource languages. |
| Approach: | They present a first publicly available Farsi dataset for fact extraction and verification . they use the construction procedure of the standard English dataset for the task . |
| Outcome: | The proposed dataset improves on the standard English dataset and is available on github. |
FoodTaxo: Generating Food Taxonomies with Large Language Models (2025.acl-industry)
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| Challenge: | a recent study shows that LLMs are useful for automating taxonomies from a seed taxonomy to a set of known concepts. |
| Approach: | They propose to use Large Language Models for automated taxonomy generation and completion. |
| Outcome: | The proposed approach is based on an open-source LLM (Llama-3). |