Sebastian Hellmann
type: Agent

Sebastian Hellman
type: Agent

Sebastian Hellmann
type: Agent

Reuters-128 Dataset in NIF
type: type
ontology location: https://github.com/NLP2RDF/ontologies/blob/master/nif-core/nif-core.ttl
media type: text/plain
type: Dataset
format: ttl
contact point: Sebastian Hellman
type: Entity
language: English
license: cc-by
type: Distribution
was generated by: http://lodlaundromat.org/.well-known/genid/d9effa3f1e3b993e07fbb987b75d9321#7
license name: Creative Commons Attribution 4.0 International
description: Extracting Linked Data following the Semantic Web paradigm from unstructured sources has become a key driver for scientific research as well as new business models. Named Entity Recognition and Disambiguation are two basic steps in this extraction process. One key driver to realize the vision of the Semantic Web and develop highly accurate tools is the availability of data for performance validation. In this article, we present three novel, manually curated and annotated corpora, N3. Furthermore, we based them on a free licence and stored in the NLP Interchange Format for interoperability reasons. See http://aksw.org/Projects/N3NERNEDNIF.html

Sebastian Hellmann
type: Agent

News-100 Corpus in NIF
type: Distribution
language: English
type: Dataset
format: ttl
license name: Creative Commons Attribution 4.0 International
media type: text/plain
ontology location: https://github.com/NLP2RDF/ontologies/blob/master/nif-core/nif-core.ttl
type: type
type: Entity
description: Extracting Linked Data following the Semantic Web paradigm from unstructured sources has become a key driver for scientific research as well as new business models. Named Entity Recognition and Disambiguation are two basic steps in this extraction process. One key driver to realize the vision of the Semantic Web and develop highly accurate tools is the availability of data for performance validation. In this article, we present three novel, manually curated and annotated corpora, N3. Furthermore, we based them on a free licence and stored in the NLP Interchange Format for interoperability reasons. See http://aksw.org/Projects/N3NERNEDNIF.html
contact point: Sebastian Hellmann
title: News-100 nif distribution
license: cc-by

RSS 500 NIF Dataset Main Distribution
license: cc-by
type: type
description: RSS 500 NIF Dataset Main Distribution
language: English
type: Distribution
type: Dataset
license name: Creative Commons Attribution 4.0 International
ontology location: https://github.com/NLP2RDF/ontologies/blob/master/nif-core/nif-core.ttl
media type: text/plain
was generated by: http://lodlaundromat.org/.well-known/genid/d9effa3f1e3b993e07fbb987b75d9321#5
format: ttl
type: Entity
contact point: Sebastian Hellmann
label: RSS 500 NIF Dataset

Kore50-nif Corpus in NIF
format: ttl
type: Distribution
type: Dataset
ontology location: https://github.com/NLP2RDF/ontologies/blob/master/nif-core/nif-core.ttl
title: kore50-nif distribution
license: cc-by
contact point: Sebastian Hellmann
language: English
type: type
media type: text/plain
type: Entity
description: Extracting Linked Data following the Semantic Web paradigm from unstructured sources has become a key driver for scientific research as well as new business models. Named Entity Recognition and Disambiguation are two basic steps in this extraction process. One key driver to realize the vision of the Semantic Web and develop highly accurate tools is the availability of data for performance validation. In this article, we present three novel, manually curated and annotated corpora, N3. Furthermore, we based them on a free licence and stored in the NLP Interchange Format for interoperability reasons. See http://aksw.org/Projects/N3NERNEDNIF.html
license name: Creative Commons Attribution 4.0 International

Sebastian Hellmann
type: Agent