@mastersthesis {1577,
	title = {Protocoles d{\textquoteright}{\'e}valuation pour l{\textquoteright}extraction d{\textquoteright}information libre},
	volume = {Ph.D.},
	year = {2019},
	month = {10/2019},
	pages = {142},
	type = {Ph.D.},
	address = {Montreal},
	abstract = {On voudrait apprendre {\`a} {\guillemotleft} lire automatiquement {\guillemotright}. L{\textquoteright}extraction d{\textquoteright}information consiste {\`a} transformer des paragraphes de texte {\'e}crits en langue naturelle en une liste d{\textquoteright}{\'e}l{\'e}ments d{\textquoteright}information autosuffisants, de fa{\c c}on {\`a} pouvoir comparer et colliger l{\textquoteright}information extraite de plusieurs sources. Les {\'e}l{\'e}ments d{\textquoteright}information sont ici repr{\'e}sent{\'e}s comme des relations entre entit{\'e}s : (Ath{\'e}na ; est la fille de ; Zeus). L{\textquoteright}extraction d{\textquoteright}information libre (EIL) est un paradigme r{\'e}cent, visant {\`a} extraire un grand nombre de relations contenues dans le texte analys{\'e}, d{\'e}couvertes au fur et {\`a} mesure, par opposition {\`a} un nombre restreint de relations pr{\'e}d{\'e}termin{\'e}es comme il est plus courant. Cette th{\`e}se porte sur l{\textquoteright}{\'e}valuation des m{\'e}thodes d{\textquoteright}EIL. Dans les deux premiers chapitres, on {\'e}value automatiquement les extractions d{\textquoteright}un syst{\`e}me d{\textquoteright}EIL, en les comparant {\`a} des r{\'e}f{\'e}rences {\'e}crites {\`a} la main, mettant respectivement l{\textquoteright}accent sur l{\textquoteright}informativit{\'e} de l{\textquoteright}extraction, puis sur son exhaustivit{\'e}. Dans les deux chapitres suivants, on {\'e}tudie et propose des alternatives {\`a} la fonction de confiance, qui juge des productions d{\textquoteright}un syst{\`e}me. En particulier, on y analyse et remet en question les m{\'e}thodologies suivant lesquelles cette fonction est {\'e}valu{\'e}e : d{\textquoteright}abord comme mod{\`e}le de validation de requ{\^e}tes, puis en comparaison du cadre bien {\'e}tabli de la compl{\'e}tion de bases de connaissances.},
	keywords = {bases de connaissances, Evaluation, extraction d{\textquoteright}information libre, knowledge base, Open information extraction},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/2019-These_William_Lechelle.pdf},
	author = {L{\'e}chelle, William}
}
@conference {1496,
	title = {An informativeness approach to Open {IE} evaluation},
	booktitle = {CICLing},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Open Information Extraction (OIE) systems extract relational tuples from text without requiring to specify in advance the relations of interest. Systems perform well on widely used metrics such as precision and yield, but a close look at systems output shows a general lack of informativeness in facts deemed correct. We propose a new evaluation protocol, based on question answering, that is closer to text understanding and end user needs. Extracted information is judged upon its capacity to automatically answer questions about the source text. As a showcase for our protocol, we devise a small corpus of question/answer pairs, and evaluate available state-of-the-art OIE systems on it. Performance-wise, our results are in line with previous findings. Furthermore, we are able to estimate recall for the task, which is novel. We distribute our annotated data and automatic evaluation program.},
	keywords = {Evaluation, Open information extraction, Question Answering},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/An_informativeness_approach_to_Open_IE_evaluation\%5B1\%5D.pdf},
	author = {L{\'e}chelle, William and Philippe Langlais},
	editor = {Gelbukh, Alexander}
}
