The Malay Lexicon Project: A database of lexical statistics for 9,592 words

Yap, Melvin J.; Liow, Susan J. Rickard; Jalil, Sajlia Binte; Faizal, Siti Syuhada Binte

doi:10.3758/brm.42.4.992

Cited by 71 publications

(62 citation statements)

References 43 publications

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“…In the English Lexicon Project (ELP), they collected naming times and lexical decision times for over 40, 000 English words from several hundred participants. Here we tested our new frequencies on English behavioral data coming from the ELP, on French behavioral data (the French Lexicon Project, or FLP; Ferrand et al, 2010), on Dutch (Keuleers, Diependaele, & Brysbaert, 2010), on Malay (the Malay Lexicon Project, or MLP; Yap et al, 2010), and on Chinese (Sze, Rickard Liow, & Yap, 2014). …”

Section: Megastudies In French English Dutch Malay and Chinesementioning

confidence: 99%

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Worldlex: Twitter and blog word frequencies for 66 languages

Gimenes

New

2015

Behav Res

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Lexical frequency is one of the strongest predictors of word processing time. The frequencies are often calculated from book-based corpora, or more recently from subtitlebased corpora. We present new frequencies based on Twitter, blog posts, or newspapers for 66 languages. We show that these frequencies predict lexical decision reaction times similar to the already existing frequencies, or even better than them. These new frequencies are freely available and may be downloaded from http://worldlex.lexique.org.Keywords Word frequency . Cross-language frequency . Twitter . BlogsThe number of occurrences of a word within a corpus is one of the best predictor of word processing time (Howes & Solomon, 1951). High-frequency words are processed more accurately and more rapidly than low-frequency words, both in comprehension and in production (Baayen, Feldman, & Schreuder, 2006;Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004;Monsell, 1991;Yap & Balota, 2009). This word frequency effect was observed in different tasks such as lexical decision tasks (Andrews & Heathcote, 2001;Balota et al., 2004), perceptual identification tasks (Grainger & Jacobs, 1996;Howes & Solomon, 1951), pronunciation tasks (Balota & Chumbley, 1985;Forster & Chambers, 1973), and semantic categorization tasks (Andrews & Heathcote, 2001;Taft & van Graan, 1998). This word frequency effect is a robust effect, since it was found in many languages.For a long time, corpora were compiled from written texts, principally books (Kučera & Francis, 1967;Thorndike & Lorge, 1944 (Kučera & Francis, 1967). Some corpora were used despite their age and despite some critics (see Brysbaert & New, 2009, for a discussion of the Kučera & Francis corpus).More recently, another source of corpora was found to be reliable: movie subtitles. The subtitle-based frequencies were first computed in French by New, Brysbaert, Véronis, and Pallier (2007). The authors showed two main results. First, they showed that the subtitle-based frequencies were a better predictor of reaction times than the book-based frequencies. Second, the subtitle-based frequencies were complementary to book-based frequencies. For instance, typical words from spoken language in everyday life were much more frequent in the subtitle-based than in the book-based corpora. Because the book-based and subtitle-based frequencies were shown to be complementary in the analyses (they explained more variance together than separately), the authors concluded that bookbased frequencies could be good estimates of written language and that subtitle-based frequencies could be good estimates of spoken language. The subtitle-based frequencies were then

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Section: Megastudies In French English Dutch Malay and Chinesementioning

confidence: 99%

“…Malay We used frequency counts from two corpora based, respectively, on a Malaysian newspaper and a Singaporean newspaper (Yap, Rickard Liow, Jalil, & Faizal, 2010).…”

Section: New Frequencies and Contextual Diversity From Twitter Blogsmentioning

confidence: 99%

Worldlex: Twitter and blog word frequencies for 66 languages

Gimenes

New

2015

Behav Res

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“…To overcome these limitations, a number of large-scale psycholinguistic databases have been made available for languages with alphabetic orthographies, including English (Balota et al, 2007;Coltheart, 1981;Keuleers, Lacey, Rastle, & Brysbaert, 2012), French (Ferrand et al, 2010), Dutch (Keuleers, Diependaele, & Brysbaert, 2010), Italian (Barca, Burani, & Arduino, 2002), and Malay (Yap, Rickard Liow, Jalil, & Faizal, 2010). For example, in English, the MRC Database provides a set of word association norms, as well as syntactic information (Coltheart, 1981).…”

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confidence: 99%

A psycholinguistic database for traditional Chinese character naming

et al. 2015

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In this study, we aimed to provide a large-scale set of psycholinguistic norms for 3,314 traditional Chinese characters, along with their naming reaction times (RTs), collected from 140 Chinese speakers. The lexical and semantic variables in the database include frequency, regularity, familiarity, consistency, number of strokes, homophone density, semantic ambiguity rating, phonetic combinability, semantic combinability, and the number of disyllabic compound words formed by a character. Multiple regression analyses were conducted to examine the predictive powers of these variables for the naming RTs. The results demonstrated that these variables could account for a significant portion of variance (55.8 %) in the naming RTs. An additional multiple regression analysis was conducted to demonstrate the effects of consistency and character frequency. Overall, the regression results were consistent with the findings of previous studies on Chinese character naming. This database should be useful for research into Chinese language processing, Chinese education, or crosslinguistic comparisons. The database can be accessed via an online inquiry system (http://ball.ling.sinica.edu.tw/ namingdatabase/index.html).Keywords Naming . Chinese character naming . Psycholinguistic norm . Chinese visual word recognition Understanding how readers process words, which are fundamental units for reading, has been a core issue in psycholinguistic studies. Behavioral studies have utilized tasks such as naming, lexical decision, speech perception, and synonym judgment, with the aim of understanding how orthographic, phonological, and semantic information is processed during visual word recognition. These experiments have typically employed a factorial design by manipulating one or two lexical variables (e.g., word frequency and spelling-to-sound consistency) in a limited set of stimuli, while trying to control for other lexical variables (e.g., orthographic neighborhood size and acoustic properties of onsets) in order to prevent any potential confounds. Although this approach has provided fruitful evidence to advance our understanding of how various lexical properties affect visual word recognition, it has some limitations in terms of how to select a set of items that vary in only one dimension and how to generalize the findings to a larger set of stimuli.To overcome these limitations, a number of large-scale psycholinguistic databases have been made available for languages with alphabetic orthographies, including English (Balota et al

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“…As a result, researchers have collected word processing times for thousands of words in so-called lexicon projects. Thus far, this happened in American English (Balota et al, 2007), Dutch , Malay (Yap, Rickard Liow, Jalil, & Faizal, 2010), French (Ferrand et al, 2010), British English (Keuleers, Lacey, Rastle, & Brysbaert, 2012), and Chinese (Sze, Rickard Liow, & Yap, in press).…”

Section: Introductionmentioning

confidence: 99%

Norms of age of acquisition and concreteness for 30,000 Dutch words

Brysbaert¹,

Stevens²,

Deyne³

et al. 2014

Acta Psychologica

162

142

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a b s t r a c t a r t i c l e i n f oWord processing studies increasingly make use of regression analyses based on large numbers of stimuli (the socalled megastudy approach) rather than experimental designs based on small factorial designs. This requires the availability of word features for many words. Following similar studies in English, we present and validate ratings of age of acquisition and concreteness for 30,000 Dutch words. These include nearly all lemmas language researchers are likely to be interested in. The ratings are freely available for research purposes.

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The Malay Lexicon Project: A database of lexical statistics for 9,592 words

Cited by 71 publications

References 43 publications

Worldlex: Twitter and blog word frequencies for 66 languages

Worldlex: Twitter and blog word frequencies for 66 languages

A psycholinguistic database for traditional Chinese character naming

Norms of age of acquisition and concreteness for 30,000 Dutch words

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