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.

CryptoPunkets #891 - CryptoPunkets

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

Concept Extraction Using Pointer–Generator Networks and Distant Supervision for Data Augmentation

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

Characterizing the hypergraph-of-entity and the structural impact of its extensions, Applied Network Science

PDF) Phonetic Matching in Japanese

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

PDF) Modelling Disagreement Between Judges for Information Retrieval System Evaluation

PDF) Hypergraph-of-entity

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

CryptoPunkets #895 - CryptoPunkets