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Hits 1 – 15 of 15

1
Data for Cyrillic Reference Parsing ...
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Data for Training and Evaluating Metadata Extraction Models based on 15 Thousand Cyrillic Script Publications ...
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3
Token-level Multilingual Epidemic Dataset for Event Extraction ...
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4
Token-level Multilingual Epidemic Dataset for Event Extraction ...
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5
Data for Training and Evaluating Metadata Extraction Models based on 15 Thousand Cyrillic Script Publications ...
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6
Data for Cyrillic Reference Parsing ...
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7
HTLinker: A Head-to-Tail Linker for Nested Named Entity Recognition
In: Symmetry ; Volume 13 ; Issue 9 (2021)
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8
A Deep Neural Network-Based Model for Named Entity Recognition for Hindi Language
In: ETSU Faculty Works (2020)
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9
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
In: Fraunhofer IAIS (2020)
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10
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
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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11
A Simple and Effective biLSTM Approach to Aspect-Based Sentiment Analysis in Social Media Customer Feedback
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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12
Semi-Markov models for sequence segmentation
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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13
Elephant: Sequence Labeling for Word and Sentence Segmentation
In: EMNLP 2013 ; https://hal.archives-ouvertes.fr/hal-01344500 ; EMNLP 2013, Oct 2013, Seattle, United States (2013)
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14
Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling
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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15
Feature-Rich Information Extraction for the Technical Trend-Map Creation
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/04-NTCIR8-PATMN-NishiyamaR.pdf
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