Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The extraction of semantic relations from texts is a hot topic. However, a large number of current methods are language and domain dependent, and the statistical and language-independent methods tend to work only with large amounts of text. This leaves out the extraction of semantic relations from standalone documents, such as single documents of unique subjects, reports from very specific domains, or small books. We propose a statistical method to extract semantic relations using clusters of concepts. Clusters are areas in the documents where concepts occur more frequently. When clusters of different concepts occur in the same areas, they may represent highly related concepts. Our method is language independent and we show comparative results for three different European languages.
Original languageUnknown
Title of host publicationProgress in Artificial Intelligence
EditorsLuís Correia, Luís Paulo Reis, José Cascalho
Place of PublicationHeidelberg
PublisherSpringer Berlin Heidelberg
Pages516-527
ISBN (Print)978-3-642-40668-3 / 978-3-642-40669-0
Publication statusPublished - 1 Jan 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Number8154
ISSN (Print)0302-9743

Keywords

    Cite this

    Silva, J. F. F. (2013). Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents. In L. Correia, L. P. Reis, & J. Cascalho (Eds.), Progress in Artificial Intelligence (pp. 516-527). (Lecture Notes in Computer Science; No. 8154). Heidelberg: Springer Berlin Heidelberg.
    Silva, Joaquim Francisco Ferreira. / Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents. Progress in Artificial Intelligence. editor / Luís Correia ; Luís Paulo Reis ; José Cascalho. Heidelberg : Springer Berlin Heidelberg, 2013. pp. 516-527 (Lecture Notes in Computer Science; 8154).
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    title = "Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents",
    abstract = "The extraction of semantic relations from texts is a hot topic. However, a large number of current methods are language and domain dependent, and the statistical and language-independent methods tend to work only with large amounts of text. This leaves out the extraction of semantic relations from standalone documents, such as single documents of unique subjects, reports from very specific domains, or small books. We propose a statistical method to extract semantic relations using clusters of concepts. Clusters are areas in the documents where concepts occur more frequently. When clusters of different concepts occur in the same areas, they may represent highly related concepts. Our method is language independent and we show comparative results for three different European languages.",
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    Silva, JFF 2013, Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents. in L Correia, LP Reis & J Cascalho (eds), Progress in Artificial Intelligence. Lecture Notes in Computer Science, no. 8154, Springer Berlin Heidelberg, Heidelberg, pp. 516-527.

    Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents. / Silva, Joaquim Francisco Ferreira.

    Progress in Artificial Intelligence. ed. / Luís Correia; Luís Paulo Reis; José Cascalho. Heidelberg : Springer Berlin Heidelberg, 2013. p. 516-527 (Lecture Notes in Computer Science; No. 8154).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    TY - CHAP

    T1 - Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents

    AU - Silva, Joaquim Francisco Ferreira

    N1 - Sem PDF

    PY - 2013/1/1

    Y1 - 2013/1/1

    N2 - The extraction of semantic relations from texts is a hot topic. However, a large number of current methods are language and domain dependent, and the statistical and language-independent methods tend to work only with large amounts of text. This leaves out the extraction of semantic relations from standalone documents, such as single documents of unique subjects, reports from very specific domains, or small books. We propose a statistical method to extract semantic relations using clusters of concepts. Clusters are areas in the documents where concepts occur more frequently. When clusters of different concepts occur in the same areas, they may represent highly related concepts. Our method is language independent and we show comparative results for three different European languages.

    AB - The extraction of semantic relations from texts is a hot topic. However, a large number of current methods are language and domain dependent, and the statistical and language-independent methods tend to work only with large amounts of text. This leaves out the extraction of semantic relations from standalone documents, such as single documents of unique subjects, reports from very specific domains, or small books. We propose a statistical method to extract semantic relations using clusters of concepts. Clusters are areas in the documents where concepts occur more frequently. When clusters of different concepts occur in the same areas, they may represent highly related concepts. Our method is language independent and we show comparative results for three different European languages.

    KW - statistics

    KW - Concepts

    KW - semantic relations

    KW - clusters

    M3 - Chapter

    SN - 978-3-642-40668-3 / 978-3-642-40669-0

    T3 - Lecture Notes in Computer Science

    SP - 516

    EP - 527

    BT - Progress in Artificial Intelligence

    A2 - Correia, Luís

    A2 - Reis, Luís Paulo

    A2 - Cascalho, José

    PB - Springer Berlin Heidelberg

    CY - Heidelberg

    ER -

    Silva JFF. Using Clusters of Concepts to Extract Semantic Relations from Standalone Documents. In Correia L, Reis LP, Cascalho J, editors, Progress in Artificial Intelligence. Heidelberg: Springer Berlin Heidelberg. 2013. p. 516-527. (Lecture Notes in Computer Science; 8154).