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Tovel: Distributed Graph Clustering for Word Sense Disambiguation
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2017 (English)In: IEEE International Conference on Data Mining Workshops, ICDMW, IEEE Computer Society, 2017, 623-630 p., 7836725Conference paper (Refereed)
Abstract [en]

Word sense disambiguation is a fundamental problem in natural language processing (NLP). In this problem, a large corpus of documents contains mentions to well-known (non-Ambiguous) words, together with mentions to ambiguous ones. The goal is to compute a clustering of the corpus, such that documents that refer to the same meaning appear in the same cluster, subsequentially, each cluster is assigned to a different semantic meaning. In this paper, we propose a mechanism for word sense disambiguation based on distributed graph clustering that is incremental in nature and can scale to big data. A novel, heuristic vertex-centric algorithm based on the metaphor of the water cycle is used to cluster the graph. Our approach is evaluated on real datasets in both centralized and decentralized environments.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017. 623-630 p., 7836725
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-208441DOI: 10.1109/ICDMW.2016.0094ISI: 000401906900086ScopusID: 2-s2.0-85015234357ISBN: 9781509054725 OAI: oai:DiVA.org:kth-208441DiVA: diva2:1106458
Conference
16th IEEE International Conference on Data Mining Workshops, ICDMW 2016, Barcelona, Spain, 12 December 2016 through 15 December 2016
Note

QC 20170607

Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-15Bibliographically approved

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Rahimian, FatemehGirdzijauskas, Sarunas
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Citation style
  • apa
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