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Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
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Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation
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Multiplicative Linear Logic from Logic Programs and Tilings
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In: https://hal.archives-ouvertes.fr/hal-02895111 ; 2021 (2021)
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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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Stellar Resolution: Multiplicatives - for the linear logician, through examples
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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Stellar Resolution: Multiplicatives - for the linear logician, through examples
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ...
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Abstract:
With the high cost of manually labeling data and the increasing interest in low-resource languages, for which human annotators might not be even available, unsupervised approaches have become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this work, we propose new fully unsupervised approaches for two tasks in morphology: unsupervised morphological segmentation and unsupervised cross-lingual part-of-speech (POS) tagging, which have been two essential subtasks for several downstream NLP applications, such as machine translation, speech recognition, information extraction and question answering. We propose a new unsupervised morphological-segmentation approach that utilizes Adaptor Grammars (AGs), nonparametric Bayesian models that generalize probabilistic context-free grammars (PCFGs), where a PCFG models word structure in the task of morphological segmentation. We implement the approach as a publicly available morphological-segmentation framework, ...
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Keyword:
Automatic speech recognition--Computer programs; Computer science; Machine translating; Question-answering systems; Speech processing systems--Computer programs
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URL: https://dx.doi.org/10.7916/d8-jd2d-9p51 https://academiccommons.columbia.edu/doi/10.7916/d8-jd2d-9p51
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