@article {1629,
	title = {Large-scale Semantic Annotation for Improved Content Discovery in a Digital Library:  A Case Study on {\'E}rudit},
	year = {2022},
	month = {08/2022},
	abstract = {In this work, we describe through a case study how two natural language processing techniques, end-to-end entity linking and Open Information Extraction (OIE), can be combined to perform semantic annotation of scholarly documents, with a view to improving the discoverability of their content. Our case study is carried out via the implementation of this hybrid approach within a fully realized prototype, Allium, whose primary goal is to facilitate content discovery and navigation by a human user within the {\'E}rudit digital library. {\'E}rudit comprises over 150 scholarly journals and 38 cultural publications in social sciences and humanities, from Qu{\'e}bec and Canada. We start by showing that entity linking allows for a solid foothold in Linked Open Data (LOD), even if it lacks in recall, especially regarding relations between entities. We then show the potential of OIE for content discovery, in part because it can complement the information gleaned from LOD. We propose and implement methods of integrating these elements to a full-fledged publication platform. Finally, we perform a system-oriented assessment and a user-oriented (human) evaluation confirming that LOD and OIE annotations are compatible and complementary for content discovery.},
	keywords = {digital libraries, exploratory search, Natural language processing, Open information extraction, semantic annotation, semantic web},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/large-scale-semantic-annotation.pdf},
	author = {Fabrizio Gotti and Philippe Langlais and Vincent Letard}
}
@proceedings {1572,
	title = {Weakly Supervised, Data-Driven Acquisition of Rules for Open Information Extraction},
	year = {2019},
	note = {Dataset available here : http://rali.iro.umontreal.ca/rali/sites/default/files/resources/misc/oie-paraphrase-rules.txt},
	month = {06/2019},
	pages = {16--28},
	publisher = {Springer},
	abstract = {We propose a way to acquire rules for Open Information Extraction, based on lemma sequence patterns (including potential ty-
pographical symbols) linking two named entities in a sentence. Rule acquisition is data-driven and requires little supervision. Given an arbitrary
relation, we identify, in a large corpus, pairs of entities that are linked by the relation and then gather, score and rank other phrases that link
the same entity pairs. We experimented with 81 relations and acquired 20 extraction rules for each by mining ClueWeb12. We devised a semi-
automatic evaluation protocol to measure recall and precision and found them to be at most 79.9\% and 62.4\% respectively. Verbal patterns are
of better quality than non-verbal ones, although the latter achieve a maximum recall of 76.5\%. The strategy proposed does not necessitate
expensive resources or time-consuming handcrafted resources, but does require a large amount of text.},
	keywords = {Natural language processing, Open information extraction, Weak Supervision},
	issn = {978-3-030-18305-9},
	doi = {10.1007/978-3-030-18305-9_2},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-18305-9_2},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/article-v5.pdf},
	author = {Fabrizio Gotti and Philippe Langlais}
}
@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 {1522,
	title = {Curating an Open Information Extraction Knowledge Base Using Games with a Purpose},
	booktitle = {Games4NLP},
	year = {2017},
	month = {2017},
	keywords = {games with a purpose, Open information extraction},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/kevin_games4nlp.pdf},
	author = {Kevin Forand and Philippe Langlais}
}
@conference {1494,
	title = {Harnessing Open Information Extraction for Entity Classification in a French Corpus},
	booktitle = {Canadian AI 2016},
	year = {2016},
	publisher = {Springer International Publishing Switzerland},
	organization = {Springer International Publishing Switzerland},
	abstract = {We describe a recall-oriented open information extraction system designed to extract knowledge from French corpora. We put it to the test by showing that general domain information triples (extracted from French Wikipedia) can be used for deriving new knowledge from domain-specific documents unrelated to Wikipedia. Specifically, we can label entity instances extracted in one corpus with the entity types identified in the other, with little supervision. We believe that the present study is the first one that focusses on such a cross-domain, recall-oriented approach in open information extraction.},
	keywords = {Entity classification, Named entities, Natural language processing, Open information extraction},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/paper.pdf},
	author = {Fabrizio Gotti and Philippe Langlais}
}
@article {1553,
	title = {From French Wikipedia to Erudit: A test case for cross-domain open information extraction},
	journal = {Computational Intelligence},
	year = {2016},
	note = {Download ReVerb for French on GitHub: https://github.com/rali-udem/reverb-french .},
	abstract = {Abstract In this paper, we describe an open information extraction pipeline based on ReVerb for extracting knowledge from French text. We put it to the test by using the information triples extracted to build an entity classifier, ie, a system able to label a given instance with its type (for instance, Michel Foucault is a philosopher). The classifier requires little supervision. One novel aspect of this study is that we show how general domain information triples (extracted from French Wikipedia) can be used for deriving new knowledge from domain-specific documents unrelated to Wikipedia, in our case scholarly articles focusing on the humanities. We believe that the present study is the first that focuses on such a cross-domain, recall-oriented approach in open information extraction. While our system{\textquoteright}s performance shows room for improvement, manual assessments show that the task is quite hard, even for a human, in part because of the cross-domain aspect of the problem we tackle.},
	keywords = {Entity classification, Named entities, Natural language processing, Open information extraction},
	doi = {10.1111/coin.12120},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/coin.12120},
	pdf = {http://rali.iro.umontreal.ca/rali/sites/default/files/publis/coin.12120.pdf},
	author = {Fabrizio Gotti and Philippe Langlais}
}
@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}
}
