Word list

Nation (Nation 1997) noted the incredible help provided by computing capabilities, making corpus analysis much easier. He cited several key issues which influence the construction of frequency lists:

• corpus representativeness
• word frequency and range
• treatment of word families
• treatment of idioms and fixed expressions
• range of information
• various other criteria

Most of currently available studies are based on written text corpus, more easily available and easy to process.

However, New et al. 2007 proposed to tap into the large number of subtitles available online to analyse large numbers of speeches. Brysbaert & New 2009 made a long critical evaluation of this traditional textual analysis approach, and support a move toward speech analysis and analysis of film subtitles available online. This has recently been followed by a handful of follow-up studies,^[1] providing valuable frequency count analysis for various languages. Indeed, the SUBTLEX movement completed in five years full studies for French (New et al. 2007), American English (Brysbaert & New 2009; Brysbaert, New & Keuleers 2012), Dutch (Keuleers & New 2010), Chinese (Cai & Brysbaert 2010), Spanish (Cuetos et al. 2011), Greek (Dimitropoulou et al. 2010), Vietnamese (Pham, Bolger & Baayen 2011), Brazil Portuguese (Tang 2012) and Portugal Portuguese (Soares et al. 2015), Albanian (Avdyli & Cuetos 2013), Polish (Mandera et al. 2014) and Catalan (2019^[2]). SUBTLEX-IT (2015) provides raw data only.^[3]

In any case, the basic "word" unit should be defined. For Latin scripts, words are usually one or several characters separated either by spaces or punctuation. But exceptions can arise, such as English "can't", French "aujourd'hui", or idioms. It may also be preferable to group words of a word family under the representation of its base word. Thus, possible, impossible, possibility are words of the same word family, represented by the base word *possib*. For statistical purpose, all these words are summed up under the base word form *possib*, allowing the ranking of a concept and form occurrence. Moreover, other languages may present specific difficulties. Such is the case of Chinese, which does not use spaces between words, and where a specified chain of several characters can be interpreted as either a phrase of unique-character words, or as a multi-character word.

It seems that Zipf's law holds for frequency lists drawn from longer texts of any natural language. Frequency lists are a useful tool when building an electronic dictionary, which is a prerequisite for a wide range of applications in computational linguistics.

German linguists define the Häufigkeitsklasse (frequency class) ${\displaystyle N}$  of an item in the list using the base 2 logarithm of the ratio between its frequency and the frequency of the most frequent item. The most common item belongs to frequency class 0 (zero) and any item that is approximately half as frequent belongs in class 1. In the example list above, the misspelled word outragious has a ratio of 76/3789654 and belongs in class 16.

${\displaystyle N=\left\lfloor 0.5-\log _{2}\left({\frac {\text{Frequency of this item}}{\text{Frequency of most common item}}}\right)\right\rfloor }$ 

where ${\displaystyle \lfloor \ldots \rfloor }$  is the floor function.

Frequency lists, together with semantic networks, are used to identify the least common, specialized terms to be replaced by their hypernyms in a process of semantic compression.

Those lists are not intended to be given directly to students, but rather to serve as a guideline for teachers and textbook authors (Nation 1997). Paul Nation's modern language teaching summary encourages first to "move from high frequency vocabulary and special purposes [thematic] vocabulary to low frequency vocabulary, then to teach learners strategies to sustain autonomous vocabulary expansion" (Nation 2006).

Word frequency is known to have various effects (Brysbaert et al. 2011; Rudell 1993). Memorization is positively affected by higher word frequency, likely because the learner is subject to more exposures (Laufer 1997). Lexical access is positively influenced by high word frequency, a phenomenon called word frequency effect (Segui et al.). The effect of word frequency is related to the effect of age-of-acquisition, the age at which the word was learned.

Below is a review of available resources.

Word counting is an ancient field,^[4] with known discussion back to Hellenistic time. In 1944, Edward Thorndike, Irvin Lorge and colleagues^[5] hand-counted 18,000,000 running words to provide the first large-scale English language frequency list, before modern computers made such projects far easier (Nation 1997). 20th century's works all suffer from their age. In particular, words relating to technology, such as "blog," which, in 2014, was #7665 in frequency^[6] in the Corpus of Contemporary American English,^[7] was first attested to in 1999,^[8][9][10] and does not appear in any of these three lists.

The Teachers Word Book of 30,000 words (Thorndike and Lorge, 1944)

The Teacher Word Book contains 30,000 lemmas or ~13,000 word families (Goulden, Nation and Read, 1990). A corpus of 18 million written words was hand analysed. The size of its source corpus increased its usefulness, but its age, and language changes, have reduced its applicability (Nation 1997).

The General Service List (West, 1953)

The General Service List contains 2,000 headwords divided into two sets of 1,000 words. A corpus of 5 million written words was analyzed in the 1940s. The rate of occurrence (%) for different meanings, and parts of speech, of the headword are provided. Various criteria, other than frequence and range, were carefully applied to the corpus. Thus, despite its age, some errors, and its corpus being entirely written text, it is still an excellent database of word frequency, frequency of meanings, and reduction of noise (Nation 1997). This list was updated in 2013 by Dr. Charles Browne, Dr. Brent Culligan and Joseph Phillips as the New General Service List.

The American Heritage Word Frequency Book (Carroll, Davies and Richman, 1971)

A corpus of 5 million running words, from written texts used in United States schools (various grades, various subject areas). Its value is in its focus on school teaching materials, and its tagging of words by the frequency of each word, in each of the school grade, and in each of the subject areas (Nation 1997).

The Brown (Francis and Kucera, 1982) LOB and related corpora

These now contain 1 million words from a written corpus representing different dialects of English. These sources are used to produce frequency lists (Nation 1997).

Traditional datasets

A review has been made by New & Pallier. An attempt was made in the 1950s–60s with the Français fondamental. It includes the F.F.1 list with 1,500 high-frequency words, completed by a later F.F.2 list with 1,700 mid-frequency words, and the most used syntax rules.^[11] It is claimed that 70 grammatical words constitute 50% of the communicatives sentence,^[12][13] while 3,680 words make about 95~98% of coverage.^[14] A list of 3,000 frequent words is available.^[15]

The French Ministry of the Education also provide a ranked list of the 1,500 most frequent word families, provided by the lexicologue Étienne Brunet.^[16] Jean Baudot made a study on the model of the American Brown study, entitled "Fréquences d'utilisation des mots en français écrit contemporain".^[17]

More recently, the project Lexique3 provides 142,000 French words, with orthography, phonetic, syllabation, part of speech, gender, number of occurrence in the source corpus, frequency rank, associated lexemes, etc., available under an open license CC-by-sa-4.0.^[18]

Subtlex

This Lexique3 is a continuous study from which originate the Subtlex movement cited above. New et al. 2007 made a completely new counting based on online film subtitles.

There have been several studies of Spanish word frequency (Cuetos et al. 2011).^[19]

Chinese corpora have long been studied from the perspective of frequency lists. The historical way to learn Chinese vocabulary is based on characters frequency (Allanic 2003). American sinologist John DeFrancis mentioned its importance for Chinese as a foreign language learning and teaching in Why Johnny Can't Read Chinese (DeFrancis 1966). As a frequency toolkit, Da (Da 1998) and the Taiwanese Ministry of Education (TME 1997) provided large databases with frequency ranks for characters and words. The HSK list of 8,848 high and medium frequency words in the People's Republic of China, and the Republic of China (Taiwan)'s TOP list of about 8,600 common traditional Chinese words are two other lists displaying common Chinese words and characters. Following the SUBTLEX movement, Cai & Brysbaert 2010 recently made a rich study of Chinese word and character frequencies.

Wiktionary:Frequency lists contains frequency lists in more languages.^[20]

Most frequently used words in different languages based on Wikipedia or combined corpora.^[21]

• Letter frequency
• Most common words in English
• Long tail
• Google Ngram Viewer – shows changes in word/phrase frequency (and relative frequency) over time

• Nation, P. (1997), "Vocabulary size, text coverage, and word lists", in Schmitt; McCarthy (eds.), Vocabulary: Description, Acquisition and Pedagogy, Cambridge: Cambridge University Press, pp. 6–19, ISBN 978-0-521-58551-4
• Laufer, B. (1997), "What's in a word that makes it hard or easy? Some intralexical factors that affect the learning of words.", Vocabulary: Description, Acquisition and Pedagogy, Cambridge: Cambridge University Press, pp. 140–155, ISBN 9780521585514
• Nation, P. (2006), "Language Education - Vocabulary", Encyclopedia of Language & Linguistics, Oxford: 494–499, doi:10.1016/B0-08-044854-2/00678-7, ISBN 9780080448541.
• Brysbaert, Marc; Buchmeier, Matthias; Conrad, Markus; Jacobs, Arthur M.; Bölte, Jens; Böhl, Andrea (2011). "The word frequency effect: a review of recent developments and implications for the choice of frequency estimates in German". Experimental Psychology. 58 (5): 412–424. doi:10.1027/1618-3169/a000123. PMID 21768069. database
• Rudell, A.P. (1993), "Frequency of word usage and perceived word difficulty : Ratings of Kucera and Francis words", Most, vol. 25, pp. 455–463
• Segui, J.; Mehler, Jacques; Frauenfelder, Uli; Morton, John (1982), "The word frequency effect and lexical access", Neuropsychologia, 20 (6): 615–627, doi:10.1016/0028-3932(82)90061-6, PMID 7162585, S2CID 39694258
• Meier, Helmut (1967), Deutsche Sprachstatistik, Hildesheim: Olms (frequency list of German words)
• DeFrancis, John (1966), Why Johnny can't read Chinese
• Allanic, Bernard (2003), The corpus of characters and their pedagogical aspect in ancient and contemporary China (fr: Les corpus de caractères et leur dimension pédagogique dans la Chine ancienne et contemporaine) (These de doctorat), Paris: INALCO

• Da, Jun (1998), Jun Da: Chinese text computing, retrieved 2010-08-21.
• Taiwan Ministry of Education (1997), 八十六年常用語詞調查報告書, retrieved 2010-08-21.
• New, Boris; Pallier, Christophe, Manuel de Lexique 3 (in French) (3.01 ed.).
• Gimenes, Manuel; New, Boris (2016), "Worldlex: Twitter and blog word frequencies for 66 languages", Behavior Research Methods, 48 (3): 963–972, doi:10.3758/s13428-015-0621-0, ISSN 1554-3528, PMID 26170053.

• New, B.; Brysbaert, M.; Veronis, J.; Pallier, C. (2007). "SUBTLEX-FR: The use of film subtitles to estimate word frequencies" (PDF). Applied Psycholinguistics. 28 (4): 661. doi:10.1017/s014271640707035x. hdl:1854/LU-599589. S2CID 145366468. Archived from the original (PDF) on 2016-10-24.
• Brysbaert, Marc; New, Boris (2009), "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" (PDF), Behavior Research Methods, 41 (4): 977–990, doi:10.3758/brm.41.4.977, PMID 19897807, S2CID 4792474
• Keuleers, E, M, B.; New, B. (2010), "SUBTLEX--NL: A new measure for Dutch word frequency based on film subtitles", Behavior Research Methods, 42 (3): 643–650, doi:10.3758/brm.42.3.643, PMID 20805586{{citation}}: CS1 maint: multiple names: authors list (link)
• Cai, Q.; Brysbaert, M. (2010), "SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles", PLOS ONE, 5 (6): 8, Bibcode:2010PLoSO...510729C, doi:10.1371/journal.pone.0010729, PMC 2880003, PMID 20532192
• Cuetos, F.; Glez-nosti, Maria; Barbón, Analía; Brysbaert, Marc (2011), "SUBTLEX-ESP : Spanish word frequencies based on film subtitles" (PDF), Psicológica, 32: 133–143
• Dimitropoulou, M.; Duñabeitia, Jon Andoni; Avilés, Alberto; Corral, José; Carreiras, Manuel (2010), "SUBTLEX-GR: Subtitle-Based Word Frequencies as the Best Estimate of Reading Behavior: The Case of Greek", Frontiers in Psychology, 1 (December): 12, doi:10.3389/fpsyg.2010.00218, PMC 3153823, PMID 21833273
• Pham, H.; Bolger, P.; Baayen, R.H. (2011), "SUBTLEX-VIE : A Measure for Vietnamese Word and Character Frequencies on Film Subtitles", ACOL
• Brysbaert, M.; New, Boris; Keuleers, E. (2012), "SUBTLEX-US : Adding Part of Speech Information to the SUBTLEXus Word Frequencies" (PDF), Behavior Research Methods: 1–22 (databases)
• Mandera, P.; Keuleers, E.; Wodniecka, Z.; Brysbaert, M. (2014). "Subtlex-pl: subtitle-based word frequency estimates for Polish" (PDF). Behav Res Methods. 47 (2): 471–483. doi:10.3758/s13428-014-0489-4. PMID 24942246. S2CID 2334688.
• Tang, K. (2012), "A 61 million word corpus of Brazilian Portuguese film subtitles as a resource for linguistic research", UCL Work Pap Linguist (24): 208–214
• Avdyli, Rrezarta; Cuetos, Fernando (June 2013), "SUBTLEX- AL: Albanian word frequencies based on film subtitles", ILIRIA International Review, 3 (1): 285–292, doi:10.21113/iir.v3i1.112 (inactive 1 November 2024), ISSN 2365-8592{{citation}}: CS1 maint: DOI inactive as of November 2024 (link)
• Soares, Ana Paula; Machado, João; Costa, Ana; Iriarte, Álvaro; Simões, Alberto; de Almeida, José João; Comesaña, Montserrat; Perea, Manuel (April 2015), "On the advantages of word frequency and contextual diversity measures extracted from subtitles: The case of Portuguese", The Quarterly Journal of Experimental Psychology, 68 (4): 680–696, doi:10.1080/17470218.2014.964271, PMID 25263599, S2CID 5376519