A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Graph-based exploration and clustering analysis of semantic spaces (2019)

Veremyev, A., Semenov, A., Pasiliao, E. L., & Boginski, V. (2019). Graph-based exploration and clustering analysis of semantic spaces. Applied Network Science, 4, Article 104. https://doi.org/10.1007/s41109-019-0228-y

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Veremyev, Alexander; Semenov, Alexander; Pasiliao, Eduardo L.; Boginski, Vladimir

Lehti tai sarja: Applied Network Science

eISSN: 2364-8228

Julkaisuvuosi: 2019

Volyymi: 4

Artikkelinumero: 104

Kustantaja: SpringerOpen

Julkaisumaa: Saksa

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/s41109-019-0228-y

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/67708


The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Specifically, we construct semantic networks based on word2vec representation of words, which is “learnt” from large text corpora (Google news, Amazon reviews), and “human built” word networks derived from the well-known lexical databases: WordNet and Moby Thesaurus. We compare “global” (e.g., degrees, distances, clustering coefficients) and “local” (e.g., most central nodes and community-type dense clusters) characteristics of considered networks. Our observations suggest that human built networks possess more intuitive global connectivity patterns, whereas local characteristics (in particular, dense clusters) of the machine built networks provide much richer information on the contextual usage and perceived meanings of words, which reveals interesting structural differences between human built and machine built semantic networks. To our knowledge, this is the first study that uses graph theory and network science in the considered context; therefore, we also provide interesting examples and discuss potential research directions that may motivate further research on the synthesis of lexicographic and machine learning based tools and lead to new insights in this area.

YSO-asiasanat: verkkoteoria; semanttinen web

Vapaat asiasanat: semantic spaces; graph theory; word2vec similarity networks; cohesive clusters; cliques; clique relaxations

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2019

JUFO-taso: 1

Viimeisin päivitys 2021-10-06 klo 13:16