5 research outputs found
The Technological Developments of the Dutch Folktale Database (1994–2016)
L’any 1994, la base de dades holandesa de contes populars va començar com una base de dades independent i es va posar en lĂnia el 2004. Des de l’any 2016 i desprĂ©s de dos projectes importants, tots els tipus de metadades es poden afegir de manera automĂ tica i semisupervisada: idiomes, noms, paraules clau, resums, subgèneres, motius i tipus de contes. Amb aquesta finalitat, la base de dades va analitzar una nova plataforma anomenada Omeka que s’adapta a les necessitats de moltes bases de dades en les humanitats, i que pot gestionar tot tipus de connectors. S’han utilitzat les tècniques segĂĽents: n-grames, detecciĂł del llenguatge, reconeixement d’entitats nombrades, extracciĂł de paraules clau, resum, bossa de paraules, aprenentatge automĂ tic i processament de llenguatge natural. A mĂ©s de MOMFER, tambĂ© s’ha afegit un motor de cerca de motius. La interpretaciĂł de dades es facilita amb els nous mitjans de visualitzaciĂł: mapes geogrĂ fics, lĂnies de temps, una xarxa de contes similars i nĂşvols de paraules. Com que la base de dades compleix els requisits de Dublin Core, es pot connectar a bases de dades similars o a un recol·lector de dades. Recentment, s’ha creat una aplicaciĂł de mineria de dades transatlĂ ntica per construir un recol·lector anomenat ISEBEL: Intelligent Search Engine for Belief Legends (motor de cerca intel·ligent de llegendes de creences). El recol·lector ha de ser capaç de buscar en una base de dades holandesa, danesa i alemanya simultĂ niament. MĂ©s endavant s'hi poden afegir altres bases de dades
DH Benelux Journal 1. Integrating Digital Humanities.
The first volume of the DH Benelux Journal. This volume includes four full-length, peer-reviewed articles that are based on accepted contributions to the 2018 DH Benelux conference in Amsterdam (The Netherlands) on Integrating Digital Humanities.
Contents:
1. Editors' Preface (Wout Dillen, Marijn Koolen, Marieke van Erp)
2. Introduction: Integrating Digital Humanities (Julie Birkholz and Gerben Zaagsma)
3. Boundary practices of digital humanities collaborations (Max Kemman)
4. Manuscripts, Metadata, and Medieval Multilingualism: Using a Manuscript Dataset to Analyze Language Use and Distribution in Medieval England (Krista A. Murchison and Ben Companjen)
5. Analysis of Fidel Castro Speeches Enhanced by Data Mining (Sergio Peignier and Patricia Zapata)
6. Character Centrality in Present-Day Dutch Literary Fiction (Roel Smeets, Eric Sanders, and Antal van den Bosch
Animacy detection in stories
Contains fulltext :
142488.pdf (publisher's version ) (Open Access)This paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word n-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted
characters which provides a unique entrance point to the collection.6th Workshop on Computational Models of Narrative (CMN-2015), 26 mei 201
Animacy detection in stories
This paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word n-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted
characters which provides a unique entrance point to the collection