| Title |
K-SPAN: A lexical database of Korean surface phonetic forms and phonological neighborhood density statistics
|
|---|---|
| Published in |
Behavior Research Methods, February 2017
|
| DOI | 10.3758/s13428-016-0836-8 |
| Pubmed ID | |
| Authors |
Jeffrey J. Holliday, Rory Turnbull, Julien Eychenne |
| Abstract |
This article presents K-SPAN (Korean Surface Phonetics and Neighborhoods), a database of surface phonetic forms and several measures of phonological neighborhood density for 63,836 Korean words. Currently publicly available Korean corpora are limited by the fact that they only provide orthographic representations in Hangeul, which is problematic since phonetic forms in Korean cannot be reliably predicted from orthographic forms. We describe the method used to derive the surface phonetic forms from a publicly available orthographic corpus of Korean, and report on several statistics calculated using this database; namely, segment unigram frequencies, which are compared to previously reported results, along with segment-based and syllable-based neighborhood density statistics for three types of representation: an "orthographic" form, which is a quasi-phonological representation, a "conservative" form, which maintains all known contrasts, and a "modern" form, which represents the pronunciation of contemporary Seoul Korean. These representations are rendered in an ASCII-encoded scheme, which allows users to query the corpus without having to read Korean orthography, and permits the calculation of a wide range of phonological measures. |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 14% |
| Netherlands | 1 | 7% |
| Japan | 1 | 7% |
| Germany | 1 | 7% |
| United Kingdom | 1 | 7% |
| Unknown | 8 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 71% |
| Scientists | 2 | 14% |
| Science communicators (journalists, bloggers, editors) | 2 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 22% |
| Student > Ph. D. Student | 5 | 22% |
| Researcher | 3 | 13% |
| Student > Doctoral Student | 2 | 9% |
| Professor | 2 | 9% |
| Other | 3 | 13% |
| Unknown | 3 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Linguistics | 8 | 35% |
| Arts and Humanities | 3 | 13% |
| Psychology | 3 | 13% |
| Neuroscience | 2 | 9% |
| Agricultural and Biological Sciences | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 3 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2022.
All research outputs
#3,416,577
of 25,382,440 outputs
Outputs from Behavior Research Methods
#424
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Outputs of similar age
#66,578
of 424,587 outputs
Outputs of similar age from Behavior Research Methods
#2
of 30 outputs
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