Lecture

Digital Humanities Lab: Practices

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In course

HUM-369: Digital humanities

Ce cours explore comment les Humanités se transforment à l'ère de l'intelligence artificielle et des données massives. Chaque séance aborde le futur d'une discipline. Les étudiants développent un prototype numérique (Vibe coding) pour accelerer la recherch ...

Description

This lecture introduces students to digital humanities through practical exercises, such as working on a large ongoing project. It covers the history of the DH Lab at EPFL, the doctoral program, and collaborations with the University of Lausanne. Students engage in activities like automatic text extraction, biographical datafication, and project presentations. The course also explores the creation of a Wikipedia-like platform called Wikipast. Through hands-on experiences, students gain insights into digital humanities tools and methodologies.

Instructors (3)

Frédéric Kaplan

Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Short biography Pro

Isabella Di Lenardo

Ph.D. in Theories and Art History. Isabella di Lenardo is senior scientist in Digital Humanities and Urban History. Her research activity is focused on digital tools and methods applied to Urban History. She is an expert in ancient cartography, city representations, cadastral sources interpreted through digital modeling, extraction and analysis systems.She's also interested in network analysis questioning the production and circulation of artistic and architectural knowledge in Europe XVIth – XVIIIth centuries in particular on North-South relationships and influences. The development of collaboration projects with European institutions and the activity carried out on various initiatives supported by the European Commission have allowed her to acquire specialism in the field of science applied to heritage, developing in particular specific skills for the enhancement of Cultural Heritage through digital tools. She leads projects in collaboration with Bibliothèque national de France (Paris), Institut National d’Histoire de l’Art (Paris), Ecole nationale des chartes (Paris), Université Paris I Panthéon-Sorbonne, Centre Allemand d’Histoire de l’Art (Paris), Musée du Louvre, Archives Nationales de Paris, Bibliothèque Historique de la Ville de Paris, Réunion des Musées Nationaux. She supervised all the urban modeling simulations for the Venice Time Machine project and acted as content curator for all the exhibitions (Venice Biennale, Grand Palais-Paris, Datasquare - ArtLab-Lausanne, Museo Correr-Venice). She was Head of “Replica” project : digitizing 1 million photos of artworks in the Fondazione Giorgio Cini (Venice) and visual patterns extraction through a search engine for visual similarities. She has a Ph.D in Art History and also studied Archaeology and Urban Studies. She has published essays and articles about Venetian Art and Urban History and participated in many Art exhibitions in European museums. She coordinated summer schools and workshops about Digital Tools for Art History and Urban History. She attended conferences on digital methods for History in universities and patrimonial institutions.

Official source

Related concepts (32)

Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

Text mining

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al.

Terminology extraction

Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc.

Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains.

Automatic summarization

Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document.

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Maud Ehrmann

Maud Ehrmann is a research scientist at EPFL’s Digital Humanities Laboratory lead by Prof. Frédéric Kaplan . She holds a PhD in Computational Linguistics from the Paris Diderot Universtiy (Paris 7) and has been engaged in a large number of scientific projects centred on information extraction and text analysis, both for present-time and historical documents. Her main research interests span Natural Language Processing and Digital Humanities and include, among others, historical text annotation, historical data processing and representation, named entity recognition, and multilingual linguistic resources creation. Her current work at the DHLAB focuses on ‘impresso - Media Monitoring of the Past’ , a SNF sinergia project she initiated with Marten Düring ( C2DH ) and Simon Clematide ( ICL ) and which aims at enabling critical analysis of historical newspapers. In addition to the overall project management, her contributions to this project include system design and data management, annotation and benchmarking and named entity processing. Besides, she participates to the activities of the Venice Time Machine , working particularly on information extraction and knowledge representation tasks. Previously, she worked on the Garzoni project where she supervised and contributed to the development of a web-based transcription and annotation interface - in collaboration with Orlin Topalov, and built a linked data-based historical knowledge base. She also contributed to the Le Temps Digital Archives project . Prior to joining the DHLAB, she worked at the Linguistics Computing Laboratory at the Sapienza University of Rome with Roberto Navigli, where she worked on the BabelNet resource - a very large multilingual encyclopaedic dictionary and semantic network - and contributed to the LIDER project. Before that, she has been working for four years at the European Commission’s Joint Research Centre in Ispra, Italy, as member of the OPTIMA unit (now Text and Data mining unit), which develops innovative and application-oriented solutions for retrieving and extracting information from the Internet with a focus on high multilinguality. Together with Erik van der Goot, Ralf Steinberger , Hristo Tanev, Leo della Rocca and many others, she contributed to the development of the Europe Media Monitor (EMM). Prior, she worked at the Xerox Europe Research Centre in Grenoble, France (now Naver Labs Europe ) in the Parsing & Semantics group led by Frédérique Segond, first as PhD candidate supported through a CIFRE grant under the supervision of Caroline Brun and Bernard Victorri , then as a post-doctoral researcher. There her research focused mainly on the automatic processing and fine-grained analysis of entities of interest, specifically named entities and temporal expressions.

Related concepts (32)

Knowledge extraction

Text mining

Terminology extraction

Information extraction

Automatic summarization