NDLI: Portuguese Part-of-Speech Tagging with Large Margin Structure Learning

Content Provider	IEEE Xplore Digital Library
Author	Rezende Fernandes, E. Muller Rodrigues, I. Luiz Milidiu, R.
Copyright Year	2014
Description	Author affiliation: DI-PUC-RIO, Rio de Janeiro, Brazil (Luiz Milidiu, R.) \|\| FACOM - UFMS, Campo Grande, Brazil (Rezende Fernandes, E.; Muller Rodrigues, I.)
Abstract	Part-of-Speech Tagging is a fundamental task on many Natural Language Processing systems. This task consists in identifying the syntactic category, i.e. the part of speech, of each word in a sentence. Despite the fact that the current state-of-the-art accuracy for this task is around 97%, any improvement has an immediate impact on more complex tasks, like Parsing, Semantic Role Labeling and Information Extraction. Thus, it is still relevant to explore this task. In this paper, we introduce a part-of-speech tagger based on the Structure Learning framework that reduces the smallest known error on the Portuguese Mac-Morpho corpus by 7.8%. We also apply our tagger to a recently revised version of Mac-Morpho. Our system accuracy on this latter version is competitive with a semi-supervised Neural Network trained on Mac-Morpho plus a very large non-annotated corpus. Additionally, our system is simpler than previous systems and uses a very limited feature set. Our system employs a Large Margin training criteria to derive a structure predictor that is more robust on unseen data.
Starting Page	25
Ending Page	30
File Size	254615
Page Count	6
File Format	PDF
ISBN	9781479956180
DOI	10.1109/BRACIS.2014.16
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-18
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Hidden Markov models Natural Language Processing Tagging Predictive models Structure Learning Prediction algorithms Natural language processing Machine Learning POS Tagging
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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