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    This study introduces a new model for crowd sequential annotations to improve sequence labeling datasets. It enhances data quality by modeling annotator expertise and efficiently inferring valid label sequences.

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    Area of Science:

    • Natural Language Processing
    • Machine Learning
    • Data Science

    Background:

    • Crowd sequential annotations offer a cost-effective method for building large sequence labeling datasets.
    • The quality of crowd sequential annotations depends on annotators' ability to capture internal dependencies within sequences.

    Purpose of the Study:

    • To propose a novel model, Sequential Annotation for Sequence Labeling with Crowds (SA-SLC), for improving crowd sequential annotations.
    • To jointly model sequential data and annotator expertise to enhance label sequence quality.

    Main Methods:

    • Developed a conditional probabilistic model incorporating categorical distributions to estimate annotator reliability in capturing label dependencies.
    • Introduced a Valid Label Sequence Inference (VLSE) method to derive ground-truth label sequences from crowd annotations by pruning subpaths.
    • VLSE enables efficient marginalization and improves the quality of derived label sequences.

    Main Results:

    • The proposed SA-SLC model effectively models annotator expertise and sequential data dependencies.
    • VLSE significantly reduces the number of candidate label sequences and enhances the quality of ground-truth label sequences.
    • Experimental results on Natural Language Processing tasks demonstrate the effectiveness of the proposed approach.

    Conclusions:

    • The SA-SLC model and VLSE method provide an effective solution for improving crowd sequential annotations in sequence labeling.
    • This approach enhances the efficiency and cost-effectiveness of creating high-quality datasets for Natural Language Processing tasks.