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On the effect of the label bias problem in part-of-speech tagging

by content 04.05.2021

Authors: Phuong Le-Hong; Xuan-Hieu Phan; The-Trung Tran

Abstract: This paper investigates the effect of the label bias problem of maximum entropy Markov models for part-of-speech tagging, a typical sequence prediction task in natural language processing. This problem has been underexploited and underappreciated. The investigation reveals useful information about the entropy of local transition probability distributions of the tagging model which enables us to exploit and quantify the label bias effect of part-of-speech tagging. Experiments on a Vietnamese treebank and on a French treebank show a significant effect of the label bias problem in both of the languages.

Published in: The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)

Date of Conference: 10-13 Nov. 2013

Conference Location: Hanoi, Vietnam

DOI: 10.1109/RIVF.2013.6719875