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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features Nerbonne, John A.; Wieling, Martijn In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 25 (2011) 3, 700-715 BLLDB OLC Linguistik Show details 2 Introducing DH 2010 Vetch, Paul; Nerbonne, John A.; Nowviskie, Bethany... In: LLC. - Oxford : Oxford Univ. Press 26 (2011) 3, 253-256 BLLDB OLC Linguistik Show details 3 Automatically extracting typical syntactic differences from corpora Wiersma, Wybo; Lauttamus, Timo; Nerbonne, John A. In: LLC. - Oxford : Oxford Univ. Press 26 (2011) 1, 107-124 BLLDB OLC Linguistik Show details 4 Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features Wieling, Martijn; Nerbonne, John In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.archives-ouvertes.fr/hal-00730283 ; Computer Speech and Language, Elsevier, 2011, 25 (3), pp.700. ⟨10.1016/j.csl.2010.05.004⟩ (2011) Abstract: International audience ; In this study we use bipartite spectral graph partitioning to simultaneously cluster varieties and identify their most distinctive linguistic features in Dutch dialect data. While clustering geographical varieties with respect to their features, e.g. pronunciation, is not new, the simultaneous identification of the features which give rise to the geographical clustering presents novel opportunities in dialectometry. Earlier methods aggregated sound differences and clustered on the basis of aggregate differences. The determination of the significant features which co-vary with cluster membership was carried out on a post hoc basis. Bipartite spectral graph clustering simultaneously seeks groups of individual features which are strongly associated, even while seeking groups of sites which share subsets of these same features. We show that the application of this method results in clear and sensible geographical groupings and discuss and analyze the importance of the concomitant features. Keyword: [SCCO.LING]Cognitive science/Linguistics; Bipartite spectral graph partitioning; Clustering; Dialectology; Dialectometry; Language variation; Sound correspondences URL: https://hal.archives-ouvertes.fr/hal-00730283/document https://hal.archives-ouvertes.fr/hal-00730283 https://hal.archives-ouvertes.fr/hal-00730283/file/PEER_stage2_10.1016%252Fj.csl.2010.05.004.pdf https://doi.org/10.1016/j.csl.2010.05.004 BASE Hide details 5 Automatically Extracting Typical Syntactic Differences from Corpora Wiersma, Wybo; Nerbonne, John; Lauttamus, Timo. - : Oxford University Press, 2011 BASE Show details 6 Introducing DH 2010 Nerbonne, John; Nowviskie, Bethany; Spence, Paul. - : Oxford University Press, 2011 BASE Show details 7 Quantitative Social Dialectology: Explaining Linguistic Variation Geographically and Socially Wieling, Martijn; Nerbonne, John; Baayen, R. Harald. - : Public Library of Science, 2011 BASE Show details

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