Matches in SemOpenAlex for { <https://semopenalex.org/work/W2785416477> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W2785416477 endingPage "135" @default.
- W2785416477 startingPage "127" @default.
- W2785416477 abstract "In dialectometry cluster analysis is a means to find groups given a set of local dialects and their mutual linguistic distances. The weakness of cluster analysis is its instability; small differences in the distance matrix may strongly change the results. Kleiweg, Nerbonne & Bosveld (2004) introduced composite cluster maps, which are obtained by collecting chances that pairs of neighboring elements are part of different clusters as indicated by the darkness of the border that is drawn between those two locations. Noise is added to the clustering process, which enables the authors to estimate about how fixed a border is. Nerbonne et al. (2008) use clustering with noise and bootstrap clustering to overcome instability. Both the work of Kleiweg, Nerbonne & Bosveld (2004) and Nerbonne et al. (2008) focus on boundaries which may be weaker or stronger. We introduce a new flavor of bootstrap clustering which generates areas, similar to classical dialect maps. We perform a procedure consisting of four steps. First, we randomly select 1,000 times n items from n items with replacement. For each resampled set of items we calculate the aggregated distances. Second, on the basis of the distances we perform agglomerative hierarchical cluster analysis. We choose nearest neighbor clustering since this method reflects the idea of dialect areas as continua. On the basis of the tree we determine the number of natural groups by means of the elbow method. Third, for each pair of dialects we count the number of times that both dialects are found in the same natural group. Fourth, when two dialects belong to the same group in more than 95% of the cases, we mark them as ‘connected.’ In this way we will obtain networks which are the groups. We apply the procedure to distances in the sound components measured with Levenhstein distance between a set of 86 Dutch dialects. We use material which was collected in the period 2008–2011." @default.
- W2785416477 created "2018-02-23" @default.
- W2785416477 creator A5047504661 @default.
- W2785416477 date "2017-01-01" @default.
- W2785416477 modified "2023-09-27" @default.
- W2785416477 title "Finding Dialect Areas by Means of Bootstrap Clustering" @default.
- W2785416477 hasPublicationYear "2017" @default.
- W2785416477 type Work @default.
- W2785416477 sameAs 2785416477 @default.
- W2785416477 citedByCount "1" @default.
- W2785416477 countsByYear W27854164772019 @default.
- W2785416477 crossrefType "journal-article" @default.
- W2785416477 hasAuthorship W2785416477A5047504661 @default.
- W2785416477 hasConcept C105795698 @default.
- W2785416477 hasConcept C115328559 @default.
- W2785416477 hasConcept C153180895 @default.
- W2785416477 hasConcept C154945302 @default.
- W2785416477 hasConcept C164866538 @default.
- W2785416477 hasConcept C17212007 @default.
- W2785416477 hasConcept C177264268 @default.
- W2785416477 hasConcept C199360897 @default.
- W2785416477 hasConcept C22648726 @default.
- W2785416477 hasConcept C23822008 @default.
- W2785416477 hasConcept C33704608 @default.
- W2785416477 hasConcept C33923547 @default.
- W2785416477 hasConcept C41008148 @default.
- W2785416477 hasConcept C73555534 @default.
- W2785416477 hasConcept C92835128 @default.
- W2785416477 hasConceptScore W2785416477C105795698 @default.
- W2785416477 hasConceptScore W2785416477C115328559 @default.
- W2785416477 hasConceptScore W2785416477C153180895 @default.
- W2785416477 hasConceptScore W2785416477C154945302 @default.
- W2785416477 hasConceptScore W2785416477C164866538 @default.
- W2785416477 hasConceptScore W2785416477C17212007 @default.
- W2785416477 hasConceptScore W2785416477C177264268 @default.
- W2785416477 hasConceptScore W2785416477C199360897 @default.
- W2785416477 hasConceptScore W2785416477C22648726 @default.
- W2785416477 hasConceptScore W2785416477C23822008 @default.
- W2785416477 hasConceptScore W2785416477C33704608 @default.
- W2785416477 hasConceptScore W2785416477C33923547 @default.
- W2785416477 hasConceptScore W2785416477C41008148 @default.
- W2785416477 hasConceptScore W2785416477C73555534 @default.
- W2785416477 hasConceptScore W2785416477C92835128 @default.
- W2785416477 hasLocation W27854164771 @default.
- W2785416477 hasOpenAccess W2785416477 @default.
- W2785416477 hasPrimaryLocation W27854164771 @default.
- W2785416477 hasRelatedWork W1967219602 @default.
- W2785416477 hasRelatedWork W1988379240 @default.
- W2785416477 hasRelatedWork W2000218803 @default.
- W2785416477 hasRelatedWork W2013588444 @default.
- W2785416477 hasRelatedWork W2031655760 @default.
- W2785416477 hasRelatedWork W2108931445 @default.
- W2785416477 hasRelatedWork W2145590631 @default.
- W2785416477 hasRelatedWork W2160893906 @default.
- W2785416477 hasRelatedWork W2186111344 @default.
- W2785416477 hasRelatedWork W2189030049 @default.
- W2785416477 hasRelatedWork W2258132172 @default.
- W2785416477 hasRelatedWork W2318043554 @default.
- W2785416477 hasRelatedWork W256356374 @default.
- W2785416477 hasRelatedWork W2946504742 @default.
- W2785416477 hasRelatedWork W3000146965 @default.
- W2785416477 hasRelatedWork W3021221386 @default.
- W2785416477 hasRelatedWork W4717855 @default.
- W2785416477 hasRelatedWork W571415012 @default.
- W2785416477 hasRelatedWork W90365499 @default.
- W2785416477 hasRelatedWork W2339758940 @default.
- W2785416477 isParatext "false" @default.
- W2785416477 isRetracted "false" @default.
- W2785416477 magId "2785416477" @default.
- W2785416477 workType "article" @default.