@inproceedings{yeghaneh-abkenar-stede-2024-neural,
title = "Neural Mining of {P}ersian Short Argumentative Texts",
author = "Yeghaneh Abkenar, Mohammad and
Stede, Manfred",
editor = "Ojha, Atul Kr. and
Ahmadi, Sina and
Cinkov{\'a}, Silvie and
Fransen, Theodorus and
Liu, Chao-Hong and
McCrae, John P.",
booktitle = "Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.eurali-1.5/",
pages = "30--35",
abstract = "Argumentation mining (AM) is concerned with extracting arguments from texts and classifying the elements (e.g.,claim and premise) and relations between them, as well as creating an argumentative structure. A significant hurdle to research in this area for the Persian language is the lack of annotated Persian language corpora. This paper introduces the first argument-annotated corpus in Persian and thereby the possibility of expanding argumentation mining to this low-resource language. The starting point is the English argumentative microtext corpus (AMT) (Peldszus and Stede, 2015), and we built the Persian variant by machine translation (MT) and careful post-editing of the output. We call this corpus Persian argumentative microtext (PAMT). Moreover, we present the first results for Argumentative Discourse Unit (ADU) classification for Persian, which is considered to be one of the main fundamental subtasks of argumentation mining. We adopted span categorization using the deep learning model of spaCy Version 3.0 (a CNN model on top of Bloom embedding with attention) on the corpus for determing argumentative units and their type (claim vs. premise)."
}
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<abstract>Argumentation mining (AM) is concerned with extracting arguments from texts and classifying the elements (e.g.,claim and premise) and relations between them, as well as creating an argumentative structure. A significant hurdle to research in this area for the Persian language is the lack of annotated Persian language corpora. This paper introduces the first argument-annotated corpus in Persian and thereby the possibility of expanding argumentation mining to this low-resource language. The starting point is the English argumentative microtext corpus (AMT) (Peldszus and Stede, 2015), and we built the Persian variant by machine translation (MT) and careful post-editing of the output. We call this corpus Persian argumentative microtext (PAMT). Moreover, we present the first results for Argumentative Discourse Unit (ADU) classification for Persian, which is considered to be one of the main fundamental subtasks of argumentation mining. We adopted span categorization using the deep learning model of spaCy Version 3.0 (a CNN model on top of Bloom embedding with attention) on the corpus for determing argumentative units and their type (claim vs. premise).</abstract>
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%0 Conference Proceedings
%T Neural Mining of Persian Short Argumentative Texts
%A Yeghaneh Abkenar, Mohammad
%A Stede, Manfred
%Y Ojha, Atul Kr.
%Y Ahmadi, Sina
%Y Cinková, Silvie
%Y Fransen, Theodorus
%Y Liu, Chao-Hong
%Y McCrae, John P.
%S Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F yeghaneh-abkenar-stede-2024-neural
%X Argumentation mining (AM) is concerned with extracting arguments from texts and classifying the elements (e.g.,claim and premise) and relations between them, as well as creating an argumentative structure. A significant hurdle to research in this area for the Persian language is the lack of annotated Persian language corpora. This paper introduces the first argument-annotated corpus in Persian and thereby the possibility of expanding argumentation mining to this low-resource language. The starting point is the English argumentative microtext corpus (AMT) (Peldszus and Stede, 2015), and we built the Persian variant by machine translation (MT) and careful post-editing of the output. We call this corpus Persian argumentative microtext (PAMT). Moreover, we present the first results for Argumentative Discourse Unit (ADU) classification for Persian, which is considered to be one of the main fundamental subtasks of argumentation mining. We adopted span categorization using the deep learning model of spaCy Version 3.0 (a CNN model on top of Bloom embedding with attention) on the corpus for determing argumentative units and their type (claim vs. premise).
%U https://aclanthology.org/2024.eurali-1.5/
%P 30-35
Markdown (Informal)
[Neural Mining of Persian Short Argumentative Texts](https://aclanthology.org/2024.eurali-1.5/) (Yeghaneh Abkenar & Stede, EURALI 2024)
ACL
- Mohammad Yeghaneh Abkenar and Manfred Stede. 2024. Neural Mining of Persian Short Argumentative Texts. In Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024, pages 30–35, Torino, Italia. ELRA and ICCL.