Literature DB >> 34405389

Database of word-level statistics for Mandarin Chinese (DoWLS-MAN).

Karl David Neergaard1, Hongzhi Xu2, James S German3, Chu-Ren Huang4.   

Abstract

In this article we present the Database of Word-Level Statistics for Mandarin Chinese (DoWLS-MAN). The database addresses the lack of agreement in phonological syllable segmentation specific to Mandarin by offering phonological features for each lexical item according to 16 schematic representations of the syllable (8 with tone and 8 without tone). Those lexical statistics that differ per phonological word and nonword due to changes in syllable segmentation are of the variant category and include subtitle lexical frequency, phonological neighborhood density measures, homophone density, and network science measures. The invariant characteristics consist of each items' lexical tone, phonological transcription, and syllable structure among others. The goal of DoWLS-MAN is to provide researchers both the ability to choose stimuli that are derived from a segmentation schema that supports an existing model of Mandarin speech processing, and the ability to choose stimuli that allow for the testing of hypotheses on phonological segmentation according to multiple schemas. In an exploratory analysis we illustrate how multiple schematic representations of the phonological mental lexicon can aid in hypothesis generation, specifically in terms of phonological processing when reading Chinese orthography. Users of the database can search among over 92,000 words, over 1600 out-of-vocabulary Chinese characters, and 4300 phonological nonwords according to either Chinese orthography, pinyin, or ASCII phonetic script. Users can also generate a list of phonological words and nonwords according to user-defined ranges and categories of lexical characteristics. DoWLS-MAN is available to the public for search or download at https://dowls.site .
© 2021. The Psychonomic Society, Inc.

Entities:  

Keywords:  Lexical database; Mandarin Chinese; Network phonology; Phonological neighborhood density; Syllable segmentation

Mesh:

Year:  2021        PMID: 34405389     DOI: 10.3758/s13428-021-01620-7

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  63 in total

1.  The role of orthography in speech production revisited.

Authors:  F-X Alario; Laetitia Perre; Caroline Castel; Johannes C Ziegler
Journal:  Cognition       Date:  2006-03-20

2.  Moving beyond Kucera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English.

Authors:  Marc Brysbaert; Boris New
Journal:  Behav Res Methods       Date:  2009-11

3.  Reading acquisition reorganizes the phonological awareness network only in alphabetic writing systems.

Authors:  Christine Brennan; Fan Cao; Nicole Pedroarena-Leal; Chris McNorgan; James R Booth
Journal:  Hum Brain Mapp       Date:  2012-07-19       Impact factor: 5.038

4.  What the reader's eye tells the mind's ear: silent reading activates inner speech.

Authors:  M Abramson; S D Goldinger
Journal:  Percept Psychophys       Date:  1997-10

5.  The influence of the phonological neighborhood clustering coefficient on spoken word recognition.

Authors:  Kit Ying Chan; Michael S Vitevitch
Journal:  J Exp Psychol Hum Percept Perform       Date:  2009-12       Impact factor: 3.332

6.  The English Lexicon Project.

Authors:  David A Balota; Melvin J Yap; Michael J Cortese; Keith A Hutchison; Brett Kessler; Bjorn Loftis; James H Neely; Douglas L Nelson; Greg B Simpson; Rebecca Treiman
Journal:  Behav Res Methods       Date:  2007-08

7.  SUBTLEX-CH: Chinese word and character frequencies based on film subtitles.

Authors:  Qing Cai; Marc Brysbaert
Journal:  PLoS One       Date:  2010-06-02       Impact factor: 3.240

8.  How children explore the phonological network in child-directed speech: A survival analysis of children's first word productions.

Authors:  Matthew T Carlson; Morgan Sonderegger; Max Bane
Journal:  J Mem Lang       Date:  2014-08       Impact factor: 3.059

9.  How Many Words Do We Know? Practical Estimates of Vocabulary Size Dependent on Word Definition, the Degree of Language Input and the Participant's Age.

Authors:  Marc Brysbaert; Michaël Stevens; Paweł Mandera; Emmanuel Keuleers
Journal:  Front Psychol       Date:  2016-07-29

Review 10.  Cracking the Code: The Impact of Orthographic Transparency and Morphological-Syllabic Complexity on Reading and Developmental Dyslexia.

Authors:  Elisabeth Borleffs; Ben A M Maassen; Heikki Lyytinen; Frans Zwarts
Journal:  Front Psychol       Date:  2019-01-04
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