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14-11-2021

Part of Speech Tagging Using Part of Speech Sequence Graph

Authors: Pejman Gholami-Dastgerdi, Mohammad-Reza Feizi-Derakhshi

Published in: Annals of Data Science | Issue 5/2023

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Abstract

The article introduces a new approach for part-of-speech tagging that utilizes a part-of-speech sequence graph to correct tags assigned by the MLE tagger. The method aims to enhance the accuracy of tagging both known and unknown words by leveraging the repetitive grammatical structures in sentences. The proposed graph-based approach addresses the limitations of traditional methods, such as the MLE tagger, which often struggles with unknown words and ambiguous tags. The article provides a detailed explanation of the methodology, including the construction of the graph and the process of traversing sentences to identify and correct tags. The results demonstrate that the proposed method outperforms existing methods, achieving a higher accuracy for both known and unknown words. The article concludes by highlighting the potential for further improvement using machine learning algorithms for selecting the best path in the graph.
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Footnotes
1
Maximum Likelihood Estimation.
 
2
A popular language in India.
 
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Metadata
Title
Part of Speech Tagging Using Part of Speech Sequence Graph
Authors
Pejman Gholami-Dastgerdi
Mohammad-Reza Feizi-Derakhshi
Publication date
14-11-2021
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 5/2023
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-021-00359-4

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