ASJP World Language Tree of Lexical Similarity:
Version 3 (July 2010)
by
André Müller, Søren Wichmann, Viveka Velupillai, Cecil H. Brown,
Pamela Brown, Sebastian Sauppe, Eric W. Holman, Dik Bakker, Johann-
Mattis List, Dmitri Egorov, Oleg Belyaev, Robert Mailhammer, Matthias
Urban, Helen Geyer, and Anthony Grant
The World Language Tree graphically illustrates relative degrees of lexical similarity
holding among 4350 of the world's languages and dialects (henceforth, languages) currently
found in the ASJP database (ASJP stands for Automated Similarity Judgment Program).
Languages branched more closely together on the ASJP tree are lexically more similar than
those branched less closely together. While most lexical resemblance charted in the tree
almost certainly is related to genetic affiliation, closely branched languages cannot routinely
be assumed to be closely genetically associated since lexical resemblance among languages
can be due to factors other than genetic relatedness (see below).
The tree is generated through use of the neighbour-joining computer algorithm
originally designed to depict phylogenetic relationships in biology (Saitou & Nei 1987). This
is implemented in MEGA 4 (Kumar et al. 2008),1 the software that we use. The algorithm is
applied to a matrix of lexical similarity scores based on Levenshtein (or edit) distances
holding between all possible pairs of the 4350 languages (for details about this, including
how we modify the Levenshtein distances for our purposes, see Bakker et al. 2009: 169). All
languages of the database are compared to one another with respect to lexical similarity
relating to their words for 40 referents determined statistically in Holman et al. (2008) to be
most stable among core vocabulary items commonly used in lexicostatistical analysis. The
tree is unrooted, but organized around a midpoint, i.e., the point which is equidistant between
the two most lexically dissimilar languages in the network. Finally, the tree is annotated to
show how it corresponds to the classification used in the latest version of the online World
Atlas of Language Structures (Haspelmath et al. 2008),2 with some updates from Dryer
(personal communication). This annotation is presented for ease of orientation, not
necessarily because ASJP agrees with it. The language names used are normally simply those
1
http://www.megasoftware.net/
2
http://wals.info/
of the sources consulted. The sources, as well as corresponding language names of
Ethnologue, are provided in a continuously updated wiki.3
Four factors influence lexical similarity registered in the tree: (1) genetic or
genealogical relationship of languages, (2) diffusion (language borrowing), (3) universal
tendencies for lexical similarity such as onomatopoeia, and (4) random variation (chance).
Languages branched closely together on the tree may be so because of strong lexical
similarity produced by any one or a combination of the four factors. Genetic relationship
would appear to be the most dominant factor accounting for close branching, followed next
by diffusion. Universal tendencies and chance are less significant contributors to close
branching than either genetic relationship or diffusion, but nonetheless clearly contribute to
the overall structure of the tree.
Typically, all languages of non-controversial language families such as Austro-
Asiatic, Uralic, or Mayan, are respectively branched together on the tree. When some
languages of a non-controversial family are not found branched together, this is because they
are substantially lexically different from other members of their family despite
unambiguously belonging to that family. Occasionally, a language can be so lexically
different from co-members of its family that it is found branched more closely with some
language or languages with which it is not genetically related at all, usually because of
chance lexical similarity or similarity due to borrowing. (When such languages are
geographically remote from one another, chance usually explains close branching.)
Typically, branching accords closely with genetic subgroups recognized by experts
within non-controversial language families. When branching is not isomorphic with
genealogical subgrouping, this often reflects diffusion among languages of the family
promoted by language contact. Thus, when used in conjunction with expert classifications of
non-controversial language families, the tree can be helpful in calling attention to historical
relationships (contact) among genetically related languages that sometimes might not be
otherwise apparent.
The tree may also suggest relationships heretofore not noticed among languages that
may be profitably investigated. For example, if two languages not known to be related in any
way are found together on a terminal branch, this may indicate a relationship between them
entailing either inheritance or contact, especially if they are not geographically remote from
one another. If the two languages are geographically distant, their close lexical similarity is
3
http://lingweb.eva.mpg.de/asjp/index.php/ASJP
2
more likely explained by chance than by either inheritance or diffusion. Also, language
isolates may join one another on a terminal branch because they have nowhere else to go in
the tree, creating the illusion that exciting, new far-flung relations may be in evidence. One
should be cautious in the interpretation of these cases.
Earlier versions of the ASJP World Language Tree did not include languages
regarded as creoles and pidgins, while this version does. Excluding creoles and pidgins would
allow human judgments to intrude into the classification (or, in this case non-classification).
It is of general interest to show how such languages pattern in the classification when
preconceived notions of how they should be treated are avoided.
References
Bakker, Dik, André Müller, Viveka Velupillai, Søren Wichmann, Cecil H. Brown, Pamela
Brown, Dmitry Egorov, Robert Mailhammer, Anthony Grant, and Eric W. Holman.
2009. Adding typology to lexicostatistics: a combined approach to language
classification. Linguistic Typology 13: 167-179.
Holman, Eric W., Søren Wichmann, Cecil H. Brown, Viveka Velupillai, André Müller, and
Dik Bakker. 2008. Explorations in automated lexicostatistics. Folia Linguistica 42.2:
331-354.
Haspelmath, Martin, Matthew S. Dryer, David Gil and Bernard Comrie. 2008. The World
Atlas of Language Structures Online. Munich: Max Planck Digital Library.
Kumar S., J. Dudley, M. Nei, and K. Tamura K. 2008. MEGA: A biologist-centric software
for evolutionary analysis of DNA and protein sequences. Briefings in Bioinformatics
9: 299-306.
Saitou, Naruya and Masatoshi Nei. 1987. The neighbor-joining method: a new method for
reconstructing phylogenetic trees. Molecular Biology and Evolution 4: 406-425.
3
Language family abbreviations
AA Afro-Asiatic Chn Chon
Aik Aikana Cho Choco
Ain Ainu Chq Chiquito
Ala Alacalufan Cht Chitimacha
Alg Algic Chu Chumash
Alt Altaic CK Chukotko-Kamchatkan
AM Amto-Musan Cmu Chimúan
An Austronesian CN Cacua-Nukak
AP Awin-Pare Cnd Candoshi
Ara Arafundi Cof Cofán
Arc Araucanian Com Comecrudan
Art Arutani Cre Creoles & Pidgins
Aru Arauan Cui Cuitlatec
Arw Arawakan CW Chapacura-Wanhan
Ata Atakapa Dos Doso
AuA Austro-Asiatic Dra Dravidian
Aus Australian EA Eskimo-Aleut
Aym Aymaran EB East Bougainville
Ban Bangi Me EBH East Bird's Head
Bar Barbacoan EGB East Geelvink Bay
Bas Basque Ele Eleman
Beo Beothuk ES East Strickland
Bil Bilua GA Great Andamanese
Bor Border Gcu Guaicuruan
Bos Bosavi GS Gogodala-Suki
Brs Burushaski Gua Guahiban
Bul Bulaka River Had Hadza
Bur Burmeso Hai Haida
Cad Caddoan Har Harakmbet
Cah Cahuapanan HM Hmong-Mien
Cam Camsá Hok Hokan
Car Cariban Hua Huavean
Cay Cayuvava Hui Huitotoan
Chi Chibchan IE Indo-European
Chk Chimakuan IG Inland Gulf
Chl Cholón Ira Irantxe
Chm Chimila Iro Iroquoian
4
Ito Itonama May Mayan
Jab Jabuti MGe Macro-Ge
Jap Japanese Mis Misumalpan
Jiv Jivaroan Mol Molof
Kad Kadugli Mom Mombum
Kam Kamula Mon Monumbo
Kap Kapixana Mos Mosetenan
Kar Karok Mov Movima
Kat Katukinan Mrw Morwap
Kau Kaure MUM Morehead and Upper Maro Rivers
Kay Kayagar Mur Mura
Ken Kenaboi Mus Muskogean
KF Kwomtari-Fas MZ Mixe-Zoque
Kho Khoisan Nah Nahali
Kiw Kiwaian Nam Nambikuaran
Kol Kolopom Nat Natchez
Kor Korean NC Niger-Congo
Krt Kartvelian NDa Nakh-Daghestanian
KT Kiowa Tanoan NDe Na-Dene
Ktn Kutenai Nim Nimboran
Kuj Kujarge Niv Nivkh
Kun Kunza NS Nilo-Saharan
Kus Kusunda NWC Northwest Caucasian
Kut Kuto OC Oregon Coast
Kwa Kwalean Odi Odiai
Kwe Kwerba Oks Oksapmin
Kwz Kwaza OM Oto-Manguean
Lav Lavukaleve Pae Paezan
Lec Leco Pan Panoan
LeM Left May Pat Pataxo
Len Lencan Pau Pauwasi
LMa Lower Mamberamo Pen Penutian
LP Lakes Plain Pui Puinave
LS Leonhard Schultze PY Peba-Yaguan
LSR Lower Sepik-Ramu Que Quechuan
Mai Mairasi Sal Salishan
Mar Marind SAn South Andamanese
Mas Mascoian Sav Savosavo
Mat Matacoan Sen Senagi
5
Sep Sepik War Warao
Sho Shom Peng Was Wasi
Sio Siouan WBg West Bougainville
Sko Sko WBm West Bomberai
Sln Salinan WF Western Fly
Slv Sáliban WP West Papuan
Snt Sentani Wsh Washo
ST Sino-Tibetan WY Wappo-Yukian
Tac Tacanan Xin Xincan
Tak Takelma Yal Yale
Tar Tarascan Yam Yamana
Tau Taushiro Yan Yanomam
Teb Teberan-Pawaian Yaw Yawa
Teq Tequistlatecan Yel Yele
Tic Ticuna Yen Yeniseian
Tim Timucua Yka Yukaghir
TK Tai-Kadai Yrb Yareban
TNG Trans-New Guinea Yrr Yaruro
TO Tor-Orya Yua Yuat
Tof Tofanma Yuc Yuchi
Tol Tol Yur Yuracare
Ton Tonkawa Yuw Yuwana
Tor Torricelli Zam Zamucoan
Tot Totonacan Zap Zaparoan
Tou Touo Zun Zuni
Tru Trumai
Tuc Tucanoan
TuK Turama-Kikorian
Tup Tupian
UA Uto-Aztecan
UC Uru-Chipaya
Un Unknown
Ura Uralic
Urr Urarina
Usk Usku
UY Upper Yuat
VJ Vaupés-Japurá
Wak Wakashan
Wao Waorani
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