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1
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios
Eskander, Ramy. - 2021
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2
Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation
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3
Multiplicative Linear Logic from Logic Programs and Tilings
In: https://hal.archives-ouvertes.fr/hal-02895111 ; 2021 (2021)
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4
A gentle introduction to Girard's Transcendental Syntax for the linear logician
In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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5
Stellar Resolution: Multiplicatives - for the linear logician, through examples
In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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6
A gentle introduction to Girard's Transcendental Syntax for the linear logician
In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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7
Stellar Resolution: Multiplicatives - for the linear logician, through examples
In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2021 (2021)
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8
A Many Small Programs (MSP) Approach in a CS1 Course
Allen, Joe Michael. - : eScholarship, University of California, 2021
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9
Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ...
Eskander, Ramy. - : Columbia University, 2021
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, ...
Keyword: Automatic speech recognition--Computer programs; Computer science; Machine translating; Question-answering systems; Speech processing systems--Computer programs
URL: https://dx.doi.org/10.7916/d8-jd2d-9p51
https://academiccommons.columbia.edu/doi/10.7916/d8-jd2d-9p51
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