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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 17 of 17 1 MasakhaNER: Named entity recognition for African languages Adelani, David,; Abbott, Jade; Neubig, Graham... 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) BASE Show details 2 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 3 Explorations in Transfer Learning for OCR Post-Correction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Neubig, Graham; Rijhwani, Shruti. - : Underline Science Inc., 2021 BASE Show details 4 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 5 Lexically Aware Semi-Supervised Learning for OCR Post-Correction ... Rijhwani, Shruti; Rosenblum, Daisy; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 6 Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Neubig, Graham. - : Underline Science Inc., 2021 BASE Show details 7 Dependency Induction Through the Lens of Visual Perception ... Su, Ruisi; Rijhwani, Shruti; Zhu, Hao. - : arXiv, 2021 BASE Show details 8 Dependency Induction Through the Lens of Visual Perception ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Hao; Bisk, Yonatan. - : Underline Science Inc., 2021 BASE Show details 9 AlloVera: a multilingual allophone database Mortensen, David,; Li, Xinjian; Littell, Patrick... 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) BASE Show details 10 AlloVera: A Multilingual Allophone Database ... Mortensen, David R.; Li, Xinjian; Littell, Patrick. - : arXiv, 2020 BASE Show details 11 A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ... Neubig, Graham; Rijhwani, Shruti; Palmer, Alexis. - : arXiv, 2020 BASE Show details 12 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 13 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 14 AlloVera: a multilingual allophone database Mortensen, David,; Li, Xinjian; Littell, Patrick... 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) BASE Show details 15 Improving Candidate Generation for Low-resource Cross-lingual Entity Linking Zhou, Shuyan; Rijhwani, Shruti; Wieting, John; Carbonell, Jaime; Neubig, Graham In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020) Abstract: Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL. 1 Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/2c64a6c204be4c2988941b57c8961921 https://doi.org/10.1162/tacl_a_00303 BASE Hide details 16 Towards Zero-resource Cross-lingual Entity Linking ... Zhou, Shuyan; Rijhwani, Shruti; Neubig, Graham. - : arXiv, 2019 BASE Show details 17 Zero-shot Neural Transfer for Cross-lingual Entity Linking ... Rijhwani, Shruti; Xie, Jiateng; Neubig, Graham. - : arXiv, 2018 BASE Show details

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