LSCP: Enhanced Large Scale Colloquial Persian Language Understanding

Hadi Abdi Khojasteh, Ebrahim Ansari, Mahdi Bohlouli


Abstract
Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a “Large Scale Colloquial Persian Dataset” (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.
Anthology ID:
2020.lrec-1.776
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6323–6327
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.776
DOI:
Bibkey:
Cite (ACL):
Hadi Abdi Khojasteh, Ebrahim Ansari, and Mahdi Bohlouli. 2020. LSCP: Enhanced Large Scale Colloquial Persian Language Understanding. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6323–6327, Marseille, France. European Language Resources Association.
Cite (Informal):
LSCP: Enhanced Large Scale Colloquial Persian Language Understanding (Abdi Khojasteh et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.776.pdf