PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus

YAWN, a system to convert the well-known and widely used Wikipedia collection into an XML corpus with semantically rich, self-explaining tags, is presented. The paper presents YAWN, a system to convert the well-known and widely used Wikipedia collection into an XML corpus with semantically rich, self-explaining tags. We introduce algorithms to annotate pages and links with concepts from the WordNet thesaurus. This annotation process exploits categorical information in Wikipedia, which is a high-quality, manually assigned source of information, extracts additional information from lists, and utilizes the invocations of templates with named parameters. We give examples how such annotations can be exploited for high-precision queries.

PDF) Hypergraph-of-entity

PDF) 8th Symposium on Languages, Applications and Technologies, SLATE 2019, June 27-28, 2019, Coimbra, Portugal

PDF] Building Linguistic Corpora from Wikipedia Articles and Discussions

PDF) Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids}

Army ANT: A Workbench for Innovation in Entity-Oriented Search

Ralf Schenkel joint work with Fabian Suchanek and Gjergji Kasneci YAWN A Semantically Annotated Wikipedia XML Corpus. - ppt herunterladen

Topic Modelling (Part 1): Creating Article Corpus from Simple Wikipedia dump

Army ANT: A Workbench for Innovation in Entity-Oriented Search

PDF] Semantic Full-Text Search with ESTER: Scalable, Easy, Fast

The language network reliably 'tracks' naturalistic meaningful non-verbal stimuli

Information_retrieval_and_extraction_IIIT

Topic Modelling (Part 1): Creating Article Corpus from Simple Wikipedia dump

Semistructured Data Search Evaluation

Build a Corpus for NLP Models from Wikipedia dump file, by Yulia Nudelman