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Concept identification of directly and indirectly related mentions referring to groups of persons


Zhukova, Anastasia; Hamborg, Felix; Donnay, Karsten; Gipp, Bela (2021). Concept identification of directly and indirectly related mentions referring to groups of persons. In: Toeppe, Katharina; Yan, Hui; Chu, Samuel Kai Wah. Diversity, divergence, dialogue. Cham: Springer, 514-526.

Abstract

Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution. We demonstrate the first results of an unsupervised approach for the identification of groups of persons as actors extracted from a set of related articles. Specifically, the approach clusters mentions of groups of persons that act as non-named entity actors in the texts, e.g., “migrant families” = “asylum-seekers.” Compared to our baseline, the approach keeps the mentions of the geopolitical entities separated, e.g., “Iran leaders” ≠ “European leaders,” and clusters (in)directly related mentions with diverse wording, e.g., “American officials” = “Trump Administration.”

Abstract

Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution. We demonstrate the first results of an unsupervised approach for the identification of groups of persons as actors extracted from a set of related articles. Specifically, the approach clusters mentions of groups of persons that act as non-named entity actors in the texts, e.g., “migrant families” = “asylum-seekers.” Compared to our baseline, the approach keeps the mentions of the geopolitical entities separated, e.g., “Iran leaders” ≠ “European leaders,” and clusters (in)directly related mentions with diverse wording, e.g., “American officials” = “Trump Administration.”

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:320 Political science
Scopus Subject Areas:Physical Sciences > Theoretical Computer Science
Physical Sciences > General Computer Science
Uncontrolled Keywords:news analysis, coreference resolution, media bias
Language:English
Date:19 March 2021
Deposited On:07 Oct 2021 16:24
Last Modified:18 Mar 2024 04:43
Publisher:Springer
ISBN:978-3-030-71292-1
Additional Information:16th International Conference, iConference 2021, Beijing, China, March 17–31, 2021, Proceedings
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/978-3-030-71292-1_40
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)