Towards Robust Linguistic Analysis using OntoNotes

By Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Zhi Zhong. 2013

512 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Dissecting Contextual Word Embeddings: Architecture and Representation (2018, background): “0 Dataset (Pradhan et al., 2013) contains predicate argument annotations for a variety of types of text, including conversation logs, web data, and biblical extracts.”
Deep Contextualized Word Representations (2018, background): “6% – a new state-of-the-art on the OntoNotes benchmark (Pradhan et al., 2013), even improving over the previous best ensemble result by 1.”
Tailor: Generating and Perturbing Text with Semantic Controls (2021, methodology): “Our experiments use the opensourced implementation of Shi and Lin (2019): demo. allennlp.org/semantic-role-labeling , with a test F1 of 86.5 on the Ontonotes 5.0 dataset (Pradhan et al., 2013).”
GLM-130B: An Open Bilingual Pre-trained Model (2022, methodology): “We create two different tasks based on semantic role labelling datasets CoNLL05 (Carreras & Màrquez, 2005), CoNLL12 (Pradhan et al., 2013), and PropBank (Kingsbury & Palmer).”
Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition (2019, methodology): “The split of the OntoNotes-5 dataset into train, development, and test sets was performed according to (Pradhan et al. 2013).”
End-to-end learning of semantic role labeling using recurrent neural networks (2015, background): “53 (Pradhan et al., 2013) and a dynamic programming algorithm for efﬁ-cient constrained inference in SRL gives F 1 = 79 .”
Deep RNNs Encode Soft Hierarchical Syntax (2018, methodology): “the classifier for each layer l separately using the development set of CoNLL-2012 and evaluate on the test set (Pradhan et al., 2013).”
Deep Semantic Role Labeling: What Works and What’s Next (2017, methodology): “We measure the performance of our SRL sys-tem on two PropBank-style, span-based SRL datasets: CoNLL-2005 (Carreras and M`arquez, 2005) and CoNLL-2012 (Pradhan et al., 2013) 3 .”
Named Entity Recognition with Bidirectional LSTM-CNNs (2015, methodology): “Evaluation was performed on the well-established CoNLL-2003 NER shared task dataset (Tjong Kim Sang and De Meulder, 2003) and the much larger but less-studied OntoNotes 5.0 dataset (Hovy et al., 2006; Pradhan et al., 2013).”
CamemBERT: a Tasty French Language Model (2019, background): “Baselines Most of the advances in NER haven been achieved on English, particularly focusing on the CoNLL 2003 (Sang and Meulder, 2003) and the Ontonotes v5 (Pradhan et al., 2012; Pradhan et al., 2013) English corpora.”
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks (2018, methodology): “For NER, we use the English portion of OntoNotes 5.0 (Pradhan et al. 2013).”
Hierarchical Contextualized Representation for Named Entity Recognition (2019, background): “0 (Pradhan et al. 2013) English NER datasets, CoNLL-2002 Spanish NER (Tjong Kim Sang 2002) dataset.”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “hand-crafted features they produced state-of-the-art results on the CoNLL-2003 [178] and OntoNotes [179], [180] data sets.”
A Survey of Deep Active Learning (2020, background): “[34, 109, 197, 199] CoNLL [184], NCBI [51], MedMentions [149], OntoNotes [167], DBLP, FZ, AG [147], Cora [228] Named entity recognition (NER) Question answering [13] CMDC [43], JabberWacky’s chatlogs9 Dialogue generation [104] Visual Genome [117], VQA [11] Visual question answer (VQA)”
DeepStruct: Pretraining of Language Models for Structure Prediction (2022, methodology): “• The CoNLL12 dataset: CoNLL12 dataset is built upon OntoNotes dataset including 39 argument types.”
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling (2018, methodology): “We experiment on the CoNLL 2005 (Carreras and M`arquez, 2005) and CoNLL 2012 (OntoNotes 5.0, (Pradhan et al., 2013)) benchmarks, using two SRL setups: end-to-end and gold predicates .”
A Unified Generative Framework for Various NER Subtasks (2021, methodology): “Therefore, as shown in Table 4, the “Word” representation with smaller average entity length in CoNLL2003, OntoNotes, CADEC, ShARe13 achieves better performance in these datasets.”