SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles
Identifieur interne : 000605 ( Main/Merge ); précédent : 000604; suivant : 000606SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles
Auteurs : Qing Cai ; Marc BrysbaertSource :
- PLoS ONE [ 1932-6203 ] ; 2010.
Abstract
Word frequency is the most important variable in language research. However, despite the growing interest in the Chinese language, there are only a few sources of word frequency measures available to researchers, and the quality is less than what researchers in other languages are used to.
Following recent work by New, Brysbaert, and colleagues in English, French and Dutch, we assembled a database of word and character frequencies based on a corpus of film and television subtitles (46.8 million characters, 33.5 million words). In line with what has been found in the other languages, the new word and character frequencies explain significantly more of the variance in Chinese word naming and lexical decision performance than measures based on written texts.
Our results confirm that word frequencies based on subtitles are a good estimate of daily language exposure and capture much of the variance in word processing efficiency. In addition, our database is the first to include information about the contextual diversity of the words and to provide good frequency estimates for multi-character words and the different syntactic roles in which the words are used. The word frequencies are freely available for research purposes.
Url:
DOI: 10.1371/journal.pone.0010729
PubMed: 20532192
PubMed Central: 2880003
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles</title><author><name sortKey="Cai, Qing" sort="Cai, Qing" uniqKey="Cai Q" first="Qing" last="Cai">Qing Cai</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Brysbaert, Marc" sort="Brysbaert, Marc" uniqKey="Brysbaert M" first="Marc" last="Brysbaert">Marc Brysbaert</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">20532192</idno><idno type="pmc">2880003</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2880003</idno><idno type="RBID">PMC:2880003</idno><idno type="doi">10.1371/journal.pone.0010729</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000183</idno><idno type="wicri:Area/Pmc/Curation">000183</idno><idno type="wicri:Area/Pmc/Checkpoint">000142</idno><idno type="wicri:Area/Ncbi/Merge">000081</idno><idno type="wicri:Area/Ncbi/Curation">000081</idno><idno type="wicri:Area/Ncbi/Checkpoint">000081</idno><idno type="wicri:Area/Main/Merge">000605</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles</title><author><name sortKey="Cai, Qing" sort="Cai, Qing" uniqKey="Cai Q" first="Qing" last="Cai">Qing Cai</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Brysbaert, Marc" sort="Brysbaert, Marc" uniqKey="Brysbaert M" first="Marc" last="Brysbaert">Marc Brysbaert</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Word frequency is the most important variable in language research. However, despite the growing interest in the Chinese language, there are only a few sources of word frequency measures available to researchers, and the quality is less than what researchers in other languages are used to.</p></sec><sec><title>Methodology</title><p>Following recent work by New, Brysbaert, and colleagues in English, French and Dutch, we assembled a database of word and character frequencies based on a corpus of film and television subtitles (46.8 million characters, 33.5 million words). In line with what has been found in the other languages, the new word and character frequencies explain significantly more of the variance in Chinese word naming and lexical decision performance than measures based on written texts.</p></sec><sec><title>Conclusions</title><p>Our results confirm that word frequencies based on subtitles are a good estimate of daily language exposure and capture much of the variance in word processing efficiency. In addition, our database is the first to include information about the contextual diversity of the words and to provide good frequency estimates for multi-character words and the different syntactic roles in which the words are used. 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