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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Explorations in Transfer Learning for OCR Post-Correction ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Lexically Aware Semi-Supervised Learning for OCR Post-Correction ...
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Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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Dependency Induction Through the Lens of Visual Perception ...
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Dependency Induction Through the Lens of Visual Perception ...
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Abstract:
Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar induction. However, as compared to dependency grammars, constituency grammars do not provide a straight- forward way to incorporate visual information without enforcing language-specific heuristics. In this paper, we propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based heuristic to jointly learn constituency-structure and dependency-structure grammars. Our experiments find that concreteness is a strong indicator for learning dependency grammars, im- proving the direct attachment score (DAS) by over 50% as compared to state-of-the-art models trained on pure text. Next, we propose an extension of our model that leverages both word concreteness ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/7j5q-9w68 https://underline.io/lecture/39855-dependency-induction-through-the-lens-of-visual-perception
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AlloVera: a multilingual allophone database
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In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ...
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Temporally-Informed Analysis of Named Entity Recognition ...
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Temporally-Informed Analysis of Named Entity Recognition ...
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AlloVera: a multilingual allophone database
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In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020)
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Zero-shot Neural Transfer for Cross-lingual Entity Linking ...
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