Difference between revisions of "Wikilinks: a Large-Scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia"
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| + | | title = Wikilinks: a Large-Scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia | ||
| + | | date = 2012 | ||
| + | | authors = [[Sameer Singh]]<br />[[Amarnag Subramanya]]<br />[[Fernando Pereira]]<br />[[Andrew McCallum]] | ||
| + | | link = https://web.cs.umass.edu/publication/docs/2012/UM-CS-2012-015.pdf | ||
| + | }} | ||
'''Wikilinks: a Large-Scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Sameer Singh]], [[Amarnag Subramanya]], [[Fernando Pereira]] and [[Andrew McCallum]]. | '''Wikilinks: a Large-Scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Sameer Singh]], [[Amarnag Subramanya]], [[Fernando Pereira]] and [[Andrew McCallum]]. | ||
== Overview == | == Overview == | ||
Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. This paper presents a method for automatically gathering massive amounts of naturally-occurring cross-document reference data. Authors also present the Wikilinks dataset comprising of 40 million mentions over 3 million entities, gathered using this method. Authors method is based on finding hyperlinks to [[Wikipedia]] from a web crawl and using anchor text as mentions. In addition to providing large-scale labeled data without human effort, authors are able to include many styles of text beyond newswire and many entity types beyond people. | Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. This paper presents a method for automatically gathering massive amounts of naturally-occurring cross-document reference data. Authors also present the Wikilinks dataset comprising of 40 million mentions over 3 million entities, gathered using this method. Authors method is based on finding hyperlinks to [[Wikipedia]] from a web crawl and using anchor text as mentions. In addition to providing large-scale labeled data without human effort, authors are able to include many styles of text beyond newswire and many entity types beyond people. | ||
Revision as of 09:28, 22 May 2020
Authors | Sameer Singh Amarnag Subramanya Fernando Pereira Andrew McCallum |
|---|---|
Publication date | 2012 |
Links | Original |
Wikilinks: a Large-Scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Sameer Singh, Amarnag Subramanya, Fernando Pereira and Andrew McCallum.
Overview
Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. This paper presents a method for automatically gathering massive amounts of naturally-occurring cross-document reference data. Authors also present the Wikilinks dataset comprising of 40 million mentions over 3 million entities, gathered using this method. Authors method is based on finding hyperlinks to Wikipedia from a web crawl and using anchor text as mentions. In addition to providing large-scale labeled data without human effort, authors are able to include many styles of text beyond newswire and many entity types beyond people.
