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Data for Cyrillic Reference Parsing ...
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Abstract:
We provide a synthetic reference data set covering over 100,000 labeled references (mostly Russian language) and a manually annotated set of real references (771 in number) gathered from multidisciplinary Cyrillic script publications . Background: Extracting structured data from bibliographic references is a crucial task for the creation of scholarly databases. While approaches, tools, and evaluation data sets for the task exist, there is a distinct lack of support for languages other than English and scripts other than the Latin alphabet. A significant portion of the scientific literature that is thereby excluded consists of publications written in Cyrillic script languages. To address this problem, we introduce a new multilingual and multidisciplinary data set of over 100,000 labeled reference strings. The data set covers multiple Cyrillic languages and contains over 700 manually labeled references, while the remaining are generated synthetically. With random samples of varying size of this data, we train ...
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Keyword:
citation data; citation field extraction; Cyrillic; digital libraries; NLP; references; scholarly data; SDU2022; sequence labeling
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URL: https://zenodo.org/record/5801913 https://dx.doi.org/10.5281/zenodo.5801913
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Data for Training and Evaluating Metadata Extraction Models based on 15 Thousand Cyrillic Script Publications ...
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Token-level Multilingual Epidemic Dataset for Event Extraction ...
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Token-level Multilingual Epidemic Dataset for Event Extraction ...
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Data for Training and Evaluating Metadata Extraction Models based on 15 Thousand Cyrillic Script Publications ...
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HTLinker: A Head-to-Tail Linker for Nested Named Entity Recognition
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In: Symmetry ; Volume 13 ; Issue 9 (2021)
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A Deep Neural Network-Based Model for Named Entity Recognition for Hindi Language
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In: ETSU Faculty Works (2020)
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NAT: Noise-Aware Training for Robust Neural Sequence Labeling
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In: Fraunhofer IAIS (2020)
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Modeling a label global context for sequence tagging in recurrent neural networks ; Modélisation d'un contexte global d'étiquettes pour l'étiquetage de séquences dans les réseaux neuronaux récurrents
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In: Journée commune AFIA-ATALA sur le Traitement Automatique des Langues et l’Intelligence Artificielle pendant la onzième édition de la plate-forme Intelligence Artificielle (PFIA 2018) ; https://hal.archives-ouvertes.fr/hal-02002111 ; Journée commune AFIA-ATALA sur le Traitement Automatique des Langues et l’Intelligence Artificielle pendant la onzième édition de la plate-forme Intelligence Artificielle (PFIA 2018), Jul 2018, Nancy, France ; https://pfia2018.loria.fr/journee-tal/ (2018)
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A Simple and Effective biLSTM Approach to Aspect-Based Sentiment Analysis in Social Media Customer Feedback
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In: Clematide, Simon (2018). A Simple and Effective biLSTM Approach to Aspect-Based Sentiment Analysis in Social Media Customer Feedback. In: Barbaresi, Adrien; Biber, Hanno; Neubarth, Friedrich; Osswald, Rainer. 14th Conference on Natural Language Processing - KONVENS 2018. Vienna: Verlag der Österreichischen Akademie der Wissenschaften, 29-33. (2018)
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Semi-Markov models for sequence segmentation
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In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007) ; http://www.aclweb.org/anthology-new/D/D07/D07-1.pdf (2015)
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Elephant: Sequence Labeling for Word and Sentence Segmentation
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In: EMNLP 2013 ; https://hal.archives-ouvertes.fr/hal-01344500 ; EMNLP 2013, Oct 2013, Seattle, United States (2013)
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Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling
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In: Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP), 2005, Vancouver, British Columbia, Canada (2005)
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Feature-Rich Information Extraction for the Technical Trend-Map Creation
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/04-NTCIR8-PATMN-NishiyamaR.pdf
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