CES Data Science: Information Extraction
In this class, we will take an overview of Information Extraction techniques and the Semantic Web. Information extraction is the process of deriving structured information (such as alive(Elvis)) from digital text (such as the sentence "Elvis is alive"). The first part of this lecture will focus on factual and semantic information extraction, i.e., we will cover named entity recognition, entity disambiguation, instance extraction, and fact extraction. The Semantic Web is the little brother of the Web that aims to represent information in a machine-readable form. So, after having learned how to extract the information from text documents, we will learn how to represent it in a semantic way. We will cover the standards RDF/S, URIs, and RDFa, and recent advances in the field. We will also touch upon applications of both Information Extraction and the Semantic Web, such as Google’s knowledge graph, IBM’s Watson question answering system, and Facebook’s Open Graph, and academic projects such as YAGO, DBpedia, and NELL.
Course title: Information Extraction
Time: Monday 2017-11-06 and Tuesday 2017-11-07
Place: Télécom ParisTech, Site Barrault, 46 rue Barrault, 75013 Paris
Evaluation: The class will be evaluated by the lab work. Every student works on their own.
- Fabian Suchanek
- Julien Romero (labs)
- Dr. Dhouha Bouamor (external lecturer from Wipolo)
Supplementary material: statistical methods
- Monday 2017-11-06: Amphi Jade
- Tuesday 2017-11-07: Room E102
- 9:00 - 10:30 Dr. Dhouha Bouamor (Wipolo)
- 10:45 - 12:15 Semantic Web
- lunch break
- 14:00-17:00 Lab in Room C45 (with Julien Romero)