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Statistical parsing of morphologically rich languages (SPMRL): what, how and whither

Tsarfaty, Reut, Seddah, Djamé, Goldberg, Yoav, Kübler, Sandra, Candito, Marie, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853, Versley, Yannick, Rehbein, Ines and Tounsi, Lamia (2010) Statistical parsing of morphologically rich languages (SPMRL): what, how and whither. In: Proceedings of the First Workshop on Statistical Parsing of Morphologically Rich Languages, 5 Jun 2010, Los Angeles, CA.

Abstract
The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:No
Uncontrolled Keywords:Morphologically Rich Languages; MRLs
Subjects:Computer Science > Computational linguistics
Humanities > Linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Copyright Information:© 2010 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:17978
Deposited On:10 Apr 2013 11:05 by Jennifer Foster . Last Modified 10 Oct 2018 15:11
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