@mastersthesis {1620,
	title = {Leveraging distant supervision for improved named entity recognition},
	volume = {PhD},
	year = {2021},
	pages = {147},
	school = {Papyrus},
	abstract = {Recent years have seen a leap in deep learning techniques that greatly changed the way Natural Language Processing (NLP) tasks are tackled. In a couple of years, neural networks and word embeddings quickly became central components to be adopted in the domain. Distant supervision (DS) is a well-used technique in NLP to produce labeled data from partially annotated examples. Traditionally, it was mainly used as training data in the absence of manual annotations, or as additional training data to improve generalization performances. In this thesis, we study how distant supervision can be employed within a modern deep learning based NLP framework. As deep learning algorithms gets better when massive amount of data is provided (especially for representation learning), we revisit the task of generating distant supervision data from Wikipedia. We apply post-processing treatments on the original dump to further increase the quantity of labeled examples, while introducing a reasonable amount of noise. Then, we explore different methods for using distant supervision data for representation learning, mainly to learn classic and contextualized word representations. Due to its importance as a basic component in many NLP applications, we choose Named-Entity Recognition (NER) as our main task. We experiment on standard NER benchmarks showing state-of-the-art performances. By doing so, we investigate a more interesting setting, that is, improving the cross-domain (generalization) performances.},
	keywords = {Distant Supervision, Generalization, NER, Wikipedia, Word Representation},
	url = {http://hdl.handle.net/1866/24799},
	author = {Ghaddar, Abbas}
}
@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}
}
