2022
DOI: 10.1109/access.2022.3188107
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Sequence Alignment Ensemble With a Single Neural Network for Sequence Labeling

Abstract: Sequence labeling, in which a class or label is assigned to each token in a given input order, is a fundamental task in natural language processing. Many advanced neural network architectures have recently been proposed to solve the sequential labeling problem affecting this task. By contrast, only a few approaches have been proposed to address the sequential ensemble problem. In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework.… Show more

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Cited by 2 publications
(1 citation statement)
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“…NLTK's default Perceptron tagger is employed due to its high accuracy compared to TNT and CRF taggers [21]. State-of-the-art POS tagging with the Penn Treebank dataset achieves an F1 Score of 98.3 [3]. Such pre-processing provides insight into syntactic- The contextualised span pair representation outputs are then classified as relation types.…”
Section: Part-of-speech Taggingmentioning
confidence: 99%
“…NLTK's default Perceptron tagger is employed due to its high accuracy compared to TNT and CRF taggers [21]. State-of-the-art POS tagging with the Penn Treebank dataset achieves an F1 Score of 98.3 [3]. Such pre-processing provides insight into syntactic- The contextualised span pair representation outputs are then classified as relation types.…”
Section: Part-of-speech Taggingmentioning
confidence: 99%