Sequential Alignment Methods for Ensemble Part-of-Speech Tagging

Jung, Jeesu; Jung, Sangkeun; Roh, Yoon-hyung

doi:10.1109/bigcomp54360.2022.00042

Cited by 3 publications

(3 citation statements)

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“…This study extended and refined the sequence alignmentbased ensemble technique introduced by Jung et al [1].…”

Section: B Sequence Ensemble With Alignmentmentioning

confidence: 82%

“…Typically, character units-specifically, four nucleotide bases, namely, adenine (A), guanine (G), thymine (T), and cytosine (C)-are used in the biology domain. However, in the natural language domain, the units for comparison should be considered carefully, such as whether to use character, character-span, or word-level units [1]. In this study, we added a sub-word level comparison unit.…”

Section: ) Sequence-block Construction Modulementioning

confidence: 99%

“…Subsequently, we developed a sequential ensemble labeling framework, called the sequential alignment labeling ensemble (SALE) framework, which has the two desired properties outlined above. The framework is an extension and refinement of the previous work by Jung et al [1]. The SALE framework 1) applies the sequence alignment method to ensemble systems to use sequential information and 2) provides multiple results from a single neural network.…”

Section: Introductionmentioning

confidence: 99%

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Sequence Alignment Ensemble With a Single Neural Network for Sequence Labeling

Jung

Seo

et al. 2022

IEEE Access

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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. Specifically, we propose a simple but efficient ensemble candidate generation framework with which multiple heterogeneous systems can easily be prepared from a single neural sequence labeling network. To evaluate the proposed framework, experiments were conducted with part-of-speech (POS) tagging and dependency label prediction problems. The results indicate that the proposed framework achieved accuracy values that were higher by 0.19 and 0.33 than those achieved by the hard-voting method on the Penn-treebank POS-tagged and Universal dependency-tagged datasets, respectively.

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“…This study extended and refined the sequence alignmentbased ensemble technique introduced by Jung et al [1].…”

Section: B Sequence Ensemble With Alignmentmentioning

confidence: 82%

Section: ) Sequence-block Construction Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sequence Alignment Ensemble With a Single Neural Network for Sequence Labeling

Jung

Seo

et al. 2022

IEEE Access

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A large-scale dataset for korean document-level relation extraction from encyclopedia texts

Son,

Lim,

Koo

et al. 2024

Appl Intell

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Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present (Toward Document-Level Relation Extraction in Korean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language’s properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis.

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