Automated Similarity Judgment Program

The Automated Similarity Judgment Program (ASJP) is a collaborative project applying computational approaches to comparative linguistics using a database of word lists. The database is open access and consists of 40-item basic-vocabulary lists for well over half of the world's languages.[1] It is continuously being expanded. In addition to isolates and languages of demonstrated genealogical groups, the database includes pidgins, creoles, mixed languages, and constructed languages. Words of the database are transcribed into a simplified standard orthography (ASJPcode).[2] The database has been used to estimate dates at which language families have diverged into daughter languages by a method related to but still different from glottochronology,[3] to determine the homeland (Urheimat) of a proto-language,[4] to investigate sound symbolism,[5] to evaluate different phylogenetic methods,[6] and several other purposes.

Automated Similarity Judgment Program
ProducerMax Planck Institute for the Science of Human History (Germany)
LanguagesEnglish
Access
CostFree
Coverage
DisciplinesQuantitative comparative linguistics
Links
Websiteasjp.clld.org

ASJP is not widely accepted among historical linguists as an adequate method to establish or evaluate relationships between language families.[7]

It is part of the Cross-Linguistic Linked Data project hosted by the Max Planck Institute for the Science of Human History.[8]

History

Original goals

ASJP was originally developed as a means for objectively evaluating the similarity of words with the same meaning from different languages, with the ultimate goal of classifying languages computationally, based on the lexical similarities observed. In the first ASJP paper[2] two semantically identical words from compared languages were judged similar if they showed at least two identical sound segments. Similarity between the two languages was calculated as a percentage of the total number of words compared that were judged as similar. This method was applied to 100-item word lists for 250 languages from language families including Austroasiatic, Indo-European, Mayan, and Muskogean.

ASJP Consortium

The ASJP Consortium, founded around 2008, came to involve around 25 professional linguists and other interested parties working as volunteer transcribers and/or extending aid to the project in other ways. The main driving force behind the founding of the consortium was Cecil H. Brown. Søren Wichmann is daily curator of the project. A third central member of the consortium is Eric W. Holman, who has created most of the software used in the project.

Shorter word lists

While word lists used were originally based on the 100-item Swadesh list, it was statistically determined that a subset of 40 of the 100 items produced just as good if not slightly better classificatory results than the whole list.[9] So subsequently word lists gathered contain only 40 items (or less, when attestations for some are lacking).

Levenshtein distance

In papers published since 2008, ASJP has employed a similarity judgment program based on Levenshtein distance (LD). This approach was found to produce better classificatory results measured against expert opinion than the method used initially. LD is defined as the minimum number of successive changes necessary to convert one word into another, where each change is the insertion, deletion, or substitution of a symbol. Within the Levenshtein approach, differences in word length can be corrected for by dividing LD by the number of symbols of the longer of the two compared words. This produces normalized LD (LDN). An LDN divided (LDND) between the two languages is calculated by dividing the average LDN for all the word pairs involving the same meaning by the average LDN for all the word pairs involving different meanings. This second normalization is intended to correct for chance similarity.[10]

Word list

The ASJP uses the following 40-word list.[11] It is similar to the Swadesh–Yakhontov list, but has some differences.

Body parts
  • eye
  • ear
  • nose
  • tongue
  • tooth
  • hand
  • knee
  • blood
  • bone
  • breast (woman’s)
  • liver
  • skin
Animals and plants
  • louse
  • dog
  • fish (noun)
  • horn (animal part)
  • tree
  • leaf
People
  • person
  • name (noun)
Nature
  • sun
  • star
  • water
  • fire
  • stone
  • path
  • mountain
  • night (dark time)
Verbs and adjectives
  • drink (verb)
  • die
  • see
  • hear
  • come
  • new
  • full
Numerals and pronouns
  • one
  • two
  • I
  • you
  • we

ASJPcode

ASJP version from 2016 uses the following symbols to encode phonemes: p b f v m w 8 t d s z c n r l S Z C j T 5 y k g x N q X h 7 L 4 G ! i e E 3 a u o

They represent 7 vowels and 34 consonants, all found on the standard QWERTY keyboard.

Sounds represented by ASJPcode [2]
ASJPcodeDescriptionIPA
ihigh front vowel, rounded and unroundedi, ɪ, y, ʏ
emid front vowel, rounded and unroundede, ø
Elow front vowel, rounded and unroundeda, æ, ɛ, ɶ, œ, e
3high and mid central vowel, rounded and unroundedɨ, ɘ, ə, ɜ, ʉ, ɵ, ɞ
alow central vowel, unroundedɐ
uhigh back vowel, rounded and unroundedɯ, u, ɑ
omid and low back vowel, rounded and unroundedɤ, ʌ, ɑ, o, ɔ, ɒ
pvoiceless bilabial stop and fricativep, ɸ
bvoiced bilabial stop and fricativeb, β
mbilabial nasalm
fvoiceless labiodental fricativef
vvoiced labiodental fricativev
8voiceless and voiced dental fricativeθ, ð
4dental nasaln̪
tvoiceless alveolar stopt
dvoiced alveolar stopd
svoiceless alveolar fricatives
zvoiced alveolar fricativez
cvoiceless and voiced alveolar affricatet͡s, d͡z
nvoiceless and voiced alveolar nasaln
Svoiceless postalveolar fricativeʃ
Zvoiced postalveolar fricativeʒ
Cvoiceless palato-alveolar affricatet͡ʃ
jvoiced palato-alveolar affricated͡ʒ
Tvoiceless and voiced palatal stopc, ɟ
5palatal nasalɲ
kvoiceless velar stopk
gvoiced velar stopɡ
xvoiceless and voiced velar fricativex, ɣ
Nvelar nasalŋ
qvoiceless uvular stopq
Gvoiced uvular stopɢ
Xvoiceless and voiced uvular fricative, voiceless and voiced pharyngeal fricativeχ, ʁ, ħ, ʕ
7voiceless glottal stopʔ
hvoiceless and voiced glottal fricativeh, ɦ
lvoiced alveolar lateral approximatel
Lall other lateralsʟ, ɭ, ʎ
wvoiced bilabial-velar approximantw
ypalatal approximantj
rvoiced apico-alveolar trill and all varieties of “r-sounds”r, ʀ, etc.
 !all varieties of “click-sounds”ǃ, ǀ, ǁ, ǂ

A ~ mark follows two consonants so that they are considered to be in the same position. Thus, kʷat becomes kw~at. Syllables like kat, wat, kaw and kwi are considered lexically similar to kw~at.

Similarly, a $ mark follows three consonants so that they are considered to be in the same position. ndy$im is considered similar to nim, dam and yim.

" marks the preceding consonant as glottalized.

See also

References
  1. Wichmann, Søren, André Müller, Annkathrin Wett, Viveka Velupillai, Julia Bischoffberger, Cecil H. Brown, Eric W. Holman, Sebastian Sauppe, Zarina Molochieva, Pamela Brown, Harald Hammarström, Oleg Belyaev, Johann-Mattis List, Dik Bakker, Dmitry Egorov, Matthias Urban, Robert Mailhammer, Agustina Carrizo, Matthew S. Dryer, Evgenia Korovina, David Beck, Helen Geyer, Patience Epps, Anthony Grant, and Pilar Valenzuela. 2013. The ASJP Database (version 16). http://asjp.clld.org/
  2. Brown, Cecil H., Eric W. Holman, Søren Wichmann, and Viveka Velupillai. 2008. Automated classification of the world's languages: A description of the method and preliminary results. STUF – Language Typology and Universals 61.4: 285-308.
  3. Holman, Eric W., Cecil H. Brown, Søren Wichmann, André Müller, Viveka Velupillai, Harald Hammarström, Sebastian Sauppe, Hagen Jung, Dik Bakker, Pamela Brown, Oleg Belyaev, Matthias Urban, Robert Mailhammer, Johann-Mattis List, and Dmitry Egorov. 2011. Automated dating of the world’s language families based on lexical similarity. Current Anthropology 52.6: 841-875.
  4. Wichmann, Søren, André Müller, and Viveka Velupillai. 2010. Homelands of the world’s language families: A quantitative approach. Diachronica 27.2: 247-276.
  5. Wichmann, Søren, Holman, Eric W., and Cecil H. Brown. 2010. Sound symbolism in basic vocabulary. Entropy 12.4: 844-858.
  6. Pompei, Simone, Vittorio Loreto, and Francesca Tria. 2011. On the accuracy of language trees. PLoS ONE 6: e20109.
  7. Cf. comments by Adelaar, Blust and Campbell in Holman, Eric W., et al. (2011) "Automated Dating of the World’s Language Families Based on Lexical Similarity." Current Anthropology, vol. 52, no. 6, pp. 841–875.
  8. "Cross-Linguistic Linked Data". Retrieved February 22, 2020.
  9. Holman, Eric W., Søren Wichmann, Cecil H. Brown, Viveka Velupillai, André Müller, and Dik Bakker. 2008. Explorations in automated language classification. Folia Linguistica 42.2: 331-354.
  10. Wichmann, Søren, Eric W. Holman, Dik Bakker, and Cecil H. Brown. 2010. Evaluating linguistic distance measures. Physica A 389: 3632-3639 (doi:10.1016/j.physa.2010.05.011).
  11. http://asjp.clld.org/static/Guidelines.pdf

Sources
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