Parser Adaptation for Social Media by Integrating Normalization

Rob van der Goot, Gertjan van Noord


Abstract
This work explores different approaches of using normalization for parser adaptation. Traditionally, normalization is used as separate pre-processing step. We show that integrating the normalization model into the parsing algorithm is more beneficial. This way, multiple normalization candidates can be leveraged, which improves parsing performance on social media. We test this hypothesis by modifying the Berkeley parser; out-of-the-box it achieves an F1 score of 66.52. Our integrated approach reaches a significant improvement with an F1 score of 67.36, while using the best normalization sequence results in an F1 score of only 66.94.
Anthology ID:
P17-2078
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
491–497
Language:
URL:
https://aclanthology.org/P17-2078
DOI:
10.18653/v1/P17-2078
Bibkey:
Cite (ACL):
Rob van der Goot and Gertjan van Noord. 2017. Parser Adaptation for Social Media by Integrating Normalization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 491–497, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Parser Adaptation for Social Media by Integrating Normalization (van der Goot & van Noord, ACL 2017)
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PDF:
https://aclanthology.org/P17-2078.pdf
Poster:
 P17-2078.Poster.pdf
Software:
 P17-2078.Software.tgz