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Distributed representation of n-gram statistics for boosting self-organizing maps with hyperdimensional computing
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.ORCID-id: 0000-0002-1313-0934
Vise andre og tillknytning
2019 (engelsk)Inngår i: Perspectives of system informatics / [ed] Nikolaj Bjørner, Irina Virbitskaite, Andrei Voronkov, Cham: Springer, 2019, s. 64-79Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents an approach for substantial reduction of the training and operating phases of Self-Organizing Maps in tasks of 2-D projection of multi-dimensional symbolic data for natural language processing such as language classification, topic extraction, and ontology development. The conventional approach for this type of problem is to use n-gram statistics as a fixed size representation for input of Self-Organizing Maps. The performance bottleneck with n-gram statistics is that the size of representation and as a result the computation time of Self-Organizing Maps grows exponentially with the size of n-grams. The presented approach is based on distributed representations of structured data using principles of hyperdimensional computing. The experiments performed on the European languages recognition task demonstrate that Self-Organizing Maps trained with distributed representations require less computations than the conventional n-gram statistics while well preserving the overall performance of Self-Organizing Maps.

sted, utgiver, år, opplag, sider
Cham: Springer, 2019. s. 64-79
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11964
Emneord [en]
Self-organizing maps, n-gram statistics, Hyperdimensional computing, Symbol strings
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-169610DOI: 10.1007/978-3-030-37487-7_6ISI: 000612725600006Scopus ID: 2-s2.0-85077499893ISBN: 978-3-030-37486-0 (tryckt)ISBN: 978-3-030-37487-7 (digital)OAI: oai:DiVA.org:umu-169610DiVA, id: diva2:1422893
Konferanse
12th International Andrei P. Ershov Informatics Conference, PSI 2019, Novosibirsk, Russia, July 2–5, 2019
Tilgjengelig fra: 2020-04-09 Laget: 2020-04-09 Sist oppdatert: 2023-09-05bibliografisk kontrollert

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