Modeling Syntactic Context Improves Morphological Segmentation
Name
Barzilay-Modeling Syntactic.pdf
Size
230.53 KB
Format
Adobe PDF
Checksum (MD5)
10f2113815d22bc436c9e3aaeae50609
Author(s)
Yoong Keok, Lee
Haghighi, Aria
Barzilay, Regina
Date Issued
June 2011
Journal
Proceedings of the Fifteenth Conference on Computational Natural Language Learning, CoNLL '11
Publisher
Association for Computing Machinery
Citation
Lee, Yoong Keok, Aria Haghighi, and Regina Barzilay. "Modeling syntactic context improves morphological segmentation." In Proceedings of the Fifteenth Conference on Computational Natural Language Learning (CoNLL '11). Association for Computational Linguistics, Portland, Oregon, USA, June 23–24, 2011. pp.1-9. ©2011 Association for Computational Linguistics.
Version
Author's final manuscript
Abstract
The connection between part-of-speech (POS) categories and morphological properties is well-documented in linguistics but underutilized in text processing systems. This paper proposes a novel model for morphological segmentation that is driven by this connection. Our model learns that words with common affixes are likely to be in the same syntactic category and uses learned syntactic categories to refine the segmentation boundaries of words. Our results demonstrate that incorporating POS categorization yields substantial performance gains on morphological segmentation of Arabic.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://dl.acm.org/citation.cfm?id=2018937
