Téléchargez le livre : Identity of Long-tail Entities in Text

Identity of Long-tail Entities in Text



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Éditeur :

SAGE Publications Ltd


Paru le : 2019-11-15



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Description
The digital era has generated a huge amount of data on the identities (profiles) of people, organizations and other entities in a digital format, largely consisting of textual documents such as news articles, encyclopedias, personal websites, books, and social media. Identity has thus been transformed from a philosophical to a societal issue, one requiring robust computational tools to determine entity identity in text. Computational systems developed to establish identity in text often struggle with long-tail cases. This book investigates how Natural Language Processing (NLP) techniques for establishing the identity of long-tail entities – which are all infrequent in communication, hardly represented in knowledge bases, and potentially very ambiguous – can be improved through the use of background knowledge. Topics covered include: distinguishing tail entities from head entities; assessing whether current evaluation datasets and metrics are representative for long-tail cases; improving evaluation of long-tail cases; accessing and enriching knowledge on long-tail entities in the Linked Open Data cloud; and investigating the added value of background knowledge (“profiling”) models for establishing the identity of NIL entities. Providing novel insights into an under-explored and difficult NLP challenge, the book will be of interest to all those working in the field of entity identification in text.
Pages
220 pages
Collection
n.c
Parution
2019-11-15
Marque
SAGE Publications Ltd
EAN papier
9781643680422
EAN PDF
9781643680439
Informations sur l'ebook
Nombre pages copiables
11
Nombre pages imprimables
11
Taille du fichier
4864 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
55,44 €

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