PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus
Por um escritor misterioso
Last updated 17 junho 2024
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://d3i71xaburhd42.cloudfront.net/39351d2d2cf32038434589099218691887cd3e18/8-Figure4-1.png)
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] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://www.researchgate.net/publication/334093997/figure/fig1/AS:774779409797122@1561733214622/Hypergraph-of-entity-base-model-representing-the-first-sentence-of-the-Wikipedia-article_Q320.jpg)
PDF) Hypergraph-of-entity
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://appliedmachinelearning.files.wordpress.com/2017/08/tree_xml2.jpg?w=584)
Topic Modelling (Part 1): Creating Article Corpus from Simple Wikipedia dump
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://christopherohge.com/presentations/bic-network-static-vis.jpg)
Publishing Scholarly Editions
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://d3i71xaburhd42.cloudfront.net/39351d2d2cf32038434589099218691887cd3e18/8-Figure4-1.png)
PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://d3i71xaburhd42.cloudfront.net/504c4850b48ae03f9669b100ba9a32a5f6297dcd/10-Figure4-1.png)
PDF] Building Linguistic Corpora from Wikipedia Articles and Discussions
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20211104141613657-0836:9781108766739:72018fig14.png?pub-status=live)
Publishing Scholarly Editions
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://www.biorxiv.org/content/biorxiv/early/2023/07/22/2022.04.24.489316/F1.large.jpg)
The language network reliably 'tracks' naturalistic meaningful non-verbal stimuli
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://i1.rgstatic.net/publication/221232895_Overview_of_the_INEX_2010_Question_Answering_Track_QAINEX/links/0c96052694aa5b5aa1000000/largepreview.png)
PDF) Overview of the INEX 2010 Question Answering Track (QA@INEX)
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://media.springernature.com/m685/springer-static/image/art%3A10.1007%2Fs41109-020-00320-z/MediaObjects/41109_2020_320_Fig10_HTML.png)
Characterizing the hypergraph-of-entity and the structural impact of its extensions, Applied Network Science
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://img.yumpu.com/18046851/1/500x640/after-half-a-century-of-slavonic-natural-language-processing.jpg)
After Half a Century of Slavonic Natural Language Processing
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://d3i71xaburhd42.cloudfront.net/39351d2d2cf32038434589099218691887cd3e18/7-Figure3-1.png)
PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://image.slidesharecdn.com/informationretrievalandextraction-140417101520-phpapp01/85/informationretrievalandextractioniiit-6-320.jpg?cb=1670650137)
Information_retrieval_and_extraction_IIIT
![PDF] YAWN: A Semantically Annotated Wikipedia XML Corpus](https://media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-030-61244-3_8/MediaObjects/497217_1_En_8_Fig3_HTML.png)
Concept Extraction Using Pointer–Generator Networks and Distant Supervision for Data Augmentation
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