OntoNotes: A Unified Relational Semantic Representation

By Sameer Pradhan, Eduard H. Hovy, Mitchell P. Marcus, Martha Palmer, Lance A. Ramshaw, Ralph M. Weischedel. 2007

217 citations recorded by the source corpus.

The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Measuring Association Between Labels and Free-Text Rationales (2020, methodology): “To demonstrate why this is the case, consider training an I → R model on a dataset with multiple annotation layers, e.g., OntoNotes, that contains word sense, predicate structure, and coreference (Pradhan et al., 2007).”
CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes (2011, methodology): “In order to organize the multiple, rich layers of annotation, the OntoNotes project has created a database representation for the raw annotation layers along with a Python API to manipulate them (Pradhan et al., 2007a).”
Smatch: an Evaluation Metric for Semantic Feature Structures (2013, methodology): “…semantic parsing in a narrow domain, while Ulf Her-mjakob 4 has developed a heuristic algorithm that exploits and supplements Ontonotes annotations (Pradhan et al., 2007) in order to automatically create AMRs for Ontonotes sentences, with a smatch score of 0.74 against human consensus AMRs.”
Evaluation of named entity coreference (2019, methodology): “Patterns in Coreference Named people, organizations and locations make up 38% of all coreference clusters in OntoNotes (Pradhan et al., 2007), yet 54% of all mentions that require coreference resolution are mentions of these types.”
Concept annotation in the CRAFT corpus (2012, methodology): “Finally, though it focuses on newswire articles, we also compare our corpus to Onto-Notes Release 2.0 here, as it is analogously a large-scale manually created corpus project with multiple types of semantic and syntactic annotation [55,56].”
Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation (2014, unknown): “The OntoNotes corpus (Pradhan et al., 2007; Weischedel et al., 2011) in the general domain, as well as the i2b2 (Uzuner et al., 2012) and THYME (Styler et al., 2014) corpora in the clinical domain would be a few examples of such emerging corpora; and (ii) ongoing proposals for reﬁning the existing…”
Referential translation machines for predicting semantic similarity (2015, unknown): “STS contain sentence pairs from different domains: sense deﬁnitions from semantic lexical resources such as OnWN [from OntoNotes (Pradhan et al. 2007) and WordNet (Miller 1995)] and FNWN [from FrameNet (Baker et al. 1998) and WordNet], news headlines, image descriptions, news title tweet comments, d”
Asking without Telling: Exploring Latent Ontologies in Contextual Representations (2020, methodology): “Extra tasks include undirected Universal Depen-dencies (Nivre et al., 2015), TAC relation classi-ﬁcation (Zhang et al., 2017), and coreference on OntoNotes (Pradhan et al., 2007).”
Abstract Meaning Representation for Sembanking (2013, background): “…the novel “The Little Prince” • 1328 sentences of web data • 1110 sentences of web data (*) • 926 sentences from Xinhua news (*) • 214 sentences from CCTV broadcast conversation (*) Collections marked with a star (*) are also in the OntoNotes corpus (Pradhan et al., 2007; Weischedel et al., 2011).”
CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes (2012, methodology): “In order to organize the multiple, rich layers of annotation, the OntoNotes project has created a database representation for the raw annotation layers along with a Python API to manipulate them (Pradhan et al., 2007a).”