eBook - PDF

Identity of Long-tail Entities in Text

Name: Identity of Long-tail Entities in Text
ISBN: 9781643680439

F. Ilievski,

Filip Ilievski,

229 pages
English
PDF
Available on iOS & Android

eBook - PDF

Identity of Long-tail Entities in Text

F. Ilievski,

Filip Ilievski,

About this book

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.

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Yes, you can access Identity of Long-tail Entities in Text by F. Ilievski, Filip Ilievski in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.