Unsupervised keyword extraction using the GoW model and centrality scores

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• dc.contributor.author Batziou, Elissavetca
• dc.contributor.author Gialampoukidis, Iliasca
• dc.contributor.author Vrochidis, Stefanosca
• dc.contributor.author Kompatsiaris, Ioannisca
• dc.date.accessioned 2018-03-02T17:44:12Z
• dc.date.available 2018-03-02T17:44:12Z
• dc.date.issued 2017
• dc.description Comunicació presentada a: The 4th International Conference, INSCI 2017, celebrat a Thessaloniki, Grècia, del 22 al 24 de novembre de 2017.ca
• dc.description.abstract Nowadays, a large amount of text documents are produced on a daily basis, so we need e cient and e ective access to their con- tent. News articles, blogs and technical reports are often lengthy, so the reader needs a quick overview of the underlying content. To that end we present graph-based models for keyword extraction, in order to compare the Bag of Words model with the Graph of Words model in the key- word extraction problem. We compare their performance in two publicly available datasets using the evaluation measures Precision@10, mean Av- erage Precision and Jaccard coe cient. The methods we have selected for comparison are grouped into two main categories. On the one hand, centrality measures on the formulated Graph-of-Words (GoW) are able to rank all words in a document from the most central to the less central, according to their score in the GoW representation. On the other hand, community detection algorithms on the GoW provide the largest commu- nity that contains the key nodes (words) in the GoW. We selected these methods as the most prominent methods to identify central nodes in a GoW model. We conclude that term-frequency scores (BoW model) are useful only in the case of less structured text, while in more structured text documents, the order of words plays a key role and graph-based models are superior to the term-frequency scores per document.en
• dc.description.sponsorship This work was supported by the projects H2020-645012 (KRISTINA) and H2020-700024 (TENSOR), funded by the European Commission.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Batziou E, Gialampoukidis I, Vrochidis S, Kompatsiaris I. Unsupervised keyword extraction using the GoW model and centrality scores. In: Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D. Internet Science. 4th International Conference, INSCI 2017 Proceedings. 2017 Nov 22-24; Thessaloniki, Greece. [Cham]: Springer, 2017. (LNCS; no. 10673). DOI: 10.1007/978-3-319-70284-1
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-70284-1
• dc.identifier.issn 0302-9743
• dc.identifier.uri http://hdl.handle.net/10230/34045
• dc.language.iso eng
• dc.publisher Springerca
• dc.relation.ispartof Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D. Internet Science. 4th International Conference, INSCI 2017 Proceedings. 2017 Nov 22-24; Thessaloniki, Greece. [Cham]: Springer, 2017. (LNCS; no. 10673).
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/700024
• dc.rights © Springer The final publication is available at Springer via https://doi.org/10.1007/978-3-319-70284-1
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Keyword-based searchen
• dc.subject.keyword Topic-based filteringen
• dc.subject.keyword Graph-based modelsen
• dc.subject.keyword Graph of Wordsen
• dc.subject.keyword Centrality measuresen
• dc.subject.keyword Community detectionen
• dc.title Unsupervised keyword extraction using the GoW model and centrality scoresca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion