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Possession identification in text

Published online by Cambridge University Press:  04 April 2018

CARMEN BANEA Open the ORCID record for CARMEN BANEA [Opens in a new window]  and
RADA MIHALCEA
CARMEN BANEA
Affiliation:
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected], [email protected]
RADA MIHALCEA
Affiliation:
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected], [email protected]
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Abstract

Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one’s most intimately held thoughts, opinions, and beliefs. This relatively new type of data is able to represent finer and more narrowly defined demographic slices. If until now researchers have primarily focused on leveraging personalized content to identify latent information such as gender, nationality, location, or age, this article seeks to establish a structured way of extracting possessions, or items that people own or are entitled to, as a way to ultimately provide insights into people’s behaviors and characteristics. We introduce the new task of ‘possession identification in text’, and release a novel dataset where possessions are marked at different confidence levels. We present experiments and results obtained when seeking to automatically identify and extract possessions from the text.

Type
Article
Information
Natural Language Engineering , Volume 24 , Issue 4 , July 2018 , pp. 589 - 610
Copyright
Copyright © Cambridge University Press 2018 

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