Information Extraction (TPT33 / INF393)
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, fact extraction, and ontological information 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/vault, IBM's Watson question answering system, and Facebook's Open Graph, and academic projects such as YAGO, DBpedia, and NELL.
Course title: Information Extraction
Course id in ATHENS: TPT33
Course id at Télécom: INF393
Location: Télécom ParisTech, 46 rue Barrault, 75013 Paris, France
Time: Monday 2014-11-16 to Friday 2014-11-20
Schedule: every day
- class 9:00-11:30 and 11:45 - 12:15
- labs 14:00 - 17:00
- class: C49, but see below
- labs: room C124
The class will be evaluated by work in the labs. The labs will be a combination of practical work (programming) and exam-like exercises. Every student works on their own. Depending on the exercise, the results are to be handed in either after the lab session or at the beginning of the next lecture.
The (anonymized) grades are now available here.
The schedule beyond the current point of time is tentative. The PDF slides are provided for convenience only, the authoritative ones are the SVG slides.