SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles
By Qing Cai, Marc Brysbaert. PLoS ONE, 2010
783 citations recorded by the source corpus.
Background 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. Methodology 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. Conclusions 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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/d0d7d3a293cfcb34e09617746dde3060ab8fcc39
How researchers see this paper
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In Artificial Intelligence
- Paweł Mandera
- Emmanuel Keuleers
- Marc Brysbaert
- Walter J. B. van Heuven
- Alessandro Lenci
- Saif M. Mohammad
- Boris New
- Ton Dijkstra
Citations in the corpus
- Subtlex-UK: A New and Improved Word Frequency Database for British English (2014, background): “This is larger than the other existing subtitle corpora (Brysbaert & New, 2009; Cai & Brysbaert, 2010; Cuetos et al., 2011; Dimitropoulou et al., 2010; Keuleers et al., 2010) 2 and allowed us to calculate more precise parts-of-speech dependent frequencies and word bigrams.”
- Electrophysiological activity predicts children's reading ability through orthographic awareness: Evidence from a cross-sectional and longitudinal study (2025, unknown): “The real characters were selected from the first-grade Chinese textbook (2016 edition) with an average frequency of 768 per million (Cai and Brysbaert, 2010).”