Toward a Multi-Representation Persian Treebank

Dehghan, Mohammad Hossein; Molla-Abbasi, Mohammad; Faili, Heshaam

doi:10.1109/istel.2018.8660996

2018 9th International Symposium on Telecommunications (IST) 2018

DOI: 10.1109/istel.2018.8660996

|View full text |Cite

Toward a Multi-Representation Persian Treebank

Mohammad Hossein Dehghan

Mohammad Molla-Abbasi

Heshaam Faili

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Cross-lingual Adaptation Using Universal Dependencies

Taghizadeh

Faili

2021

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. The idea of UD parsing is to capture similarities as well as idiosyncrasies among typologically different languages. In this article, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages. We study two tasks of paraphrase identification and relation extraction as case studies. Based on UD parse trees, we develop several models using tree kernels and show that these models trained on the English dataset can correctly classify data of other languages, e.g., French, Farsi, and Arabic. The proposed approach opens up avenues for exploiting UD parsing in solving similar cross-lingual tasks, which is very useful for languages for which no labeled data is available.

show abstract

Cross-lingual Adaptation Using Universal Dependencies

Taghizadeh

Faili

2021

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Toward a Multi-Representation Persian Treebank

Cited by 1 publication

References 14 publications

Cross-lingual Adaptation Using Universal Dependencies

Cross-lingual Adaptation Using Universal Dependencies

Contact Info

Product

Resources

About