@mastersthesis {1516,
	title = {Coreference Resolution with and for Wikipedia},
	volume = {Masters},
	year = {2016},
	month = {2016},
	pages = {74},
	school = {Universit{\'e} de Montr{\'e}al},
	type = {Masters},
	address = {Montr{\'e}al},
	abstract = {Wikipedia is a resource of choice exploited in many NLP applications, yet we are not aware of recent attempts to adapt coreference resolution to this resource, a preliminary step to understand Wikipedia texts. The first part of this master thesis is to build
an English coreference corpus, where all documents are from the English version of Wikipedia. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Our corpus has no restriction on the topics of the documents
being annotated, and documents of various sizes have been considered for annotation. Our annotation scheme follows the one of OntoNotes with a few disparities. In part two, we propose a testbed for evaluating coreference systems in a simple task of measuring
the particulars of the concept described in aWikipedia page (eg. The statements of President Obama the Wikipedia page dedicated to that person). We show that by exploiting the Wikipedia markup (categories, redirects, infoboxes, etc.) of a document, as well
as links to external knowledge bases such as Freebase (information of the type, number, type of relationship with other entities, etc.), we can acquire useful information on entities that helps to classify mentions as coreferent or not.},
	keywords = {Coreference Resolution, Corpus Creation, Wikipedia},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/Ghaddar_These.pdf},
	author = {Abbas Ghaddar}
}
