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.

Frontiers Using machine learning to evaluate 1.2 million studies on small-scale farming and post-production food systems in low- and middle-income countries

PDF) The Methodology of Manual Assessment in the Evaluation of Link Discovery

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

PDF) Hypergraph-of-entity

Information_retrieval_and_extraction_IIIT

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

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

Language Models Learn Sentiment and Substance from 11,000 Psychoactive Experiences

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

PDF] Building Linguistic Corpora from Wikipedia Articles and Discussions