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Identity of Long-tail Entities in Text

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Title: Identity of Long-tail Entities in Text
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.
Authors: Filip Ilievski
Resource Type: eBook.
Subjects: Natural language processing (Computer science)
Categories: COMPUTERS / Artificial Intelligence / General
Database: eBook Collection (EBSCOhost)
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  Data: 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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      – Code: 006.35
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Natural language processing (Computer science)
        Type: general
    Titles:
      – TitleFull: Identity of Long-tail Entities in Text
        Type: main
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          Name:
            NameFull: Filip Ilievski
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            NameFull: Filip Ilievski
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2019
            – D: 15
              M: 01
              Type: profile
              Y: 2020
          Identifiers:
            – Type: isbn-print
              Value: 9781643680422
            – Type: isbn-electronic
              Value: 9781643680439
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              Value: 00043
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            – TitleFull: Identity of Long-tail Entities in Text
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