Learning to extract folktale keywords

Manually assigned keywords provide a valuable means for accessing large document collections. They can serve as a shallow document summary and enable more efficient retrieval and aggregation of information. In this paper we investigate keywords in the context of the Dutch Folktale Database, a large collection of stories including fairy tales, jokes and urban legends. We carry out a quantitative and qualitative analysis of the keywords in the collection. Up to 80% of the assigned keywords (or a minor variation) appear in the text itself. Human annotators show moderate to substantial agreement in their judgment of keywords. Finally, we evaluate a learning to rank approach to extract and rank keyword candidates. We conclude that this is a promising approach to automate this time intensive task

Электронный архив мифологических рассказов Пермского края: реализация системы интеллектуального поиска

Работа выполнена в рамках проекта РГНФ № 14-34-01279а2

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 línia el 2004. Des de l’any 2016 i després de dos projectes importants, tots els tipus de metadades es poden afegir de manera automàtica 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 següents: n-grames, detecció del llenguatge, reconeixement d’entitats nombrades, extracció de paraules clau, resum, bossa de paraules, aprenentatge automàtic i processament de llenguatge natural. A més de MOMFER, també s’ha afegit un motor de cerca de motius. La interpretació de dades es facilita amb els nous mitjans de visualització: mapes geogràfics, línies de temps, una xarxa de contes similars i núvols 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 aplicació de mineria de dades transatlàntica 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 simultàniament. Més endavant s'hi poden afegir altres bases de dades

Supporting the Exploration of Online Cultural Heritage Collections:The Case of the Dutch Folktale Database

This paper demonstrates the use of a user-centred design approach for the development of generous interfaces/rich prospect browsers for an online cultural heritage collection, determining its primary user groups and designing different browsing tools to cater to their specific needs. We set out to solve a set of problems faced by many online cultural heritage collections. These problems are lack of accessibility, limited functionalities to explore the collection through browsing, and risk of less known content being overlooked. The object of our study is the Dutch Folktale Database, an online collection of tens of thousands of folktales from the Netherlands. Although this collection was designed as a research commodity for folktale experts, its primary user group consists of casual users from the general public. We present the new interfaces we developed to facilitate browsing and exploration of the collection by both folktale experts and casual users. We focus on the user-centred design approach we adopted to develop interfaces that would fit the users' needs and preferences.</p

Supporting the Exploration of Online Cultural Heritage Collections:The Case of the Dutch Folktale Database

This paper demonstrates the use of a user-centred design approach for the development of generous interfaces/rich prospect browsers for an online cultural heritage collection, determining its primary user groups and designing different browsing tools to cater to their specific needs. We set out to solve a set of problems faced by many online cultural heritage collections. These problems are lack of accessibility, limited functionalities to explore the collection through browsing, and risk of less known content being overlooked. The object of our study is the Dutch Folktale Database, an online collection of tens of thousands of folktales from the Netherlands. Although this collection was designed as a research commodity for folktale experts, its primary user group consists of casual users from the general public. We present the new interfaces we developed to facilitate browsing and exploration of the collection by both folktale experts and casual users. We focus on the user-centred design approach we adopted to develop interfaces that would fit the users' needs and preferences

Learning to extract folktale keywords

Manually assigned keywords provide a valuable means for accessing large document collections. They can serve as a shallow document summary and enable more efficient retrieval and aggregation of information. In this paper we investigate keywords in the context of the Dutch Folktale Database, a large collection of stories including fairy tales, jokes and urban legends. We carry out a quantitative and qualitative analysis of the keywords in the collection. Up to 80% of the assigned keywords (or a minor variation) appear in the text itself. Human annotators show moderate to substantial agreement in their judgment of keywords. Finally, we evaluate a learning to rank approach to extract and rank keyword candidates. We conclude that this is a promising approach to automate this time intensive task