Microsoft Word - Pairwise comparisons of typological profiles6.doc
Pairwise comparisons of typological profiles 
Søren Wichmann 
Max Planck Institute for Evolutionary Anthropology & Leiden University 
Eric W. Holman 
University of California, Los Angeles 
0. Introduction
Being rare or ‘exotic’ is a relative phenomenon. From a Samoan point of view Burushaski is an 
extremely exotic language, but from the point of view of Telugu much less so. In this brief note we 
want to look a how different and how similar languages turn out to be in pairwise comparisons and 
the role that genealogical relatedness plays in this regard. We are interested in knowing whether 
there is a cut-off point Shigh in the amount of similarities such that we can be sure that language pairs 
that  have more than Shigh similarities are all generally thought to be related and also whether there 
is a cut-of point Slow at the other end of the scale such that all languages having less similarities than 
Slow are thought to be unrelated. In other words, if a language is ‘normal’ relative to some other 
language (as Burushaski is to Telugu), does this imply that the two languages are related according 
to commonly accepted classifications? Or, if two languages are mutually very exotic (as Burushaski 
and Samoan), does this imply that they are not thought to be related in commonly accepted 
classifications? 
 The data we use, as well as the genealogical classification, are from the World Atlas of 
Language Structures (Haspelmath et al., ed., henceforth WALS). The conclusions must of course be 
seen in relation to this particular dataset. Thus, when we observe a certain amount of typological 
similarity between two languages, this is strictly and only similarity in terms of the kinds of features 
investigated in WALS. The dataset includes 134 nonredundant features, each of which has from two 
to nine discrete values. All of these are quite generic typological features. Our conclusions are also 
limited to the amount of data available. We have required that for any language pair in our sample 
there should be 45 or more features attested for both members of the pair (a motivation for this 
precise number follows shortly). This has limited our sample to 320 languages and 29,810 pairs of 
languages compared. Among these pairs, there are 1,099 which are related according to the 
                                                           
 We would like to thank Bernard Comrie, Cecil Brown, and Dietrich Stauffer for comments on this manuscript. 
classification used in WALS. Henceforth we substitute ‘related’ for the more cumbersome ‘related 
according to the WALS classification’. We follow this classification because it seeks to meet a 
consensus view. 
1. Results 
Figure 1 presents the overall results of the investigation. As can be seen, the more similar languages 
get, the greater the probability is that they are related. The figures on which the curve is based are 
presented in Table 1. Percent similarity was defined as the percentage of attested features for which 
both languages have the same value. We have binned language pairs in 5% intervals from 10% to 
90% similarity. For the plot in Figure 1 the mean percent similarity in each interval was used. Table 
1 gives some additional information: it also shows how many language pairs belong in each 
interval. This is important for the interpretation of the results, as we shall see shortly. 
Before giving our interpretation let us explain why we have chosen the criterion that 
language pairs should have 45 or more features attested for both languages. It turns out that for a 
criterion of 30 or more features the curve is rather similar but not quite as steep, showing less 
dependence between the amount of similarity and the probability of finding related pairs. This 
indicates that the fewer features one operates with, the more prominent is random sampling 
variability in percent similarity. When operating with a criterion of 60 or more attested features the 
curve becomes uneven, indicating that the higher criterion passes too few pairs for stable results. 
This becomes even more pronounced when the criterion is 75 or more features. Obviously, with a 
more extended database the number of features taken to be criterial could be raised, but 45 is a 
number that suits the data available in WALS.  
Figure 1. The probability of finding related languages 
Probability of finding related languages
0 10 20 30 40 50 60 70 80 90 100
% similarity
Table 1. Data. (%SIM = % typological similarity between members of pairs; %REL = % of 
language pairs that are related; PAIRS = number of language pairs in range) 
%SIM %REL PAIRS 
10.0-14.9 0 11 
15.0-19.9 0 91 
20.0-24.9 0 443 
25.0-29.9 0.26 1566 
30.0-34.9 0.33 3904 
35.0-39.9 0.4 6019 
40.0-44.9 1.2 6772 
45.0-49.9 3.26 4873 
50.0-54.9 6.68 3520 
55.0-59.9 15.41 1551 
60.0-64.9 23.72 666 
65.0-69.9 38.24 238 
70.0-74.9 54.26 94 
75.0-79.9 61.54 39 
80.0-84.9 85 20 
85.0-89.9 100 2 
90.0-94.9 100 1 
 It may be of interest to mention the language pairs that fall in the lower and upper 
ranges of the percentage of shared values. Collectors of linguistic trivia may find it interesting that 
the members of the most divergent language pair in the world (in our dataset), i.e. Tümpisa 
Shoshone and Wari’, are found in the same general area, namely the Americas, that someone who is 
tired of Romance linguistics should turn to Nivkh and someone fed up with Swedish should visit 
the Koasatis when looking for something as radically different as it gets. Lists of the 20 most 
divergent language pairs and the 20 most similar ones are provided in tables 2 and 3. 
Table 2. The 20 most divergent language pairs in the sample 
Language A Language B Number of 
features 
compared 
% Similarity 
Tümpisa 
Shoshone   
Wari'              48 10.4 
Archi              Tukang Besi        46 13 
Maybrat            Limbu              45 13.3 
Italian            Nivkh              51 13.7 
Burushaski         Samoan             49 14.3 
Tzutujil           Burmese            49 14.3 
Ju|'hoan           Yup'ik (Central)   56 14.3 
Maybrat            Tamil              55 14.5 
Nubian 
(Dongolese) 
Acehnese           48 14.6 
Swedish            Koasati            47 14.9 
Klamath            Wari'              47 14.9 
Kongo              Ladakhi            46 15.2 
Bashkir            Maori              46 15.2 
Berber (Middle 
Atlas) 
Waorani            45 15.6 
Lango              Archi              45 15.6 
Archi              Thai               45 15.6 
Thai               Retuarã            45 15.6 
Ijo (Kolokuma)     Kutenai            50 16 
Kongo              Evenki             56 16.1 
Arabic (Egyptian)  Tümpisa 
Shoshone   
48 16.7 
Table 3. The 20 most similar language pairs in the sample 
Language A Language B Relatedness Number of 
features 
compared 
% Similarity 
Lango              Luo                same genus 46 80.4 
Luvale             Zulu               same genus 97 80.4 
Khmer              Vietnamese         same family, 
different genera 
89 80.9 
Vietnamese         Thai               different families 110 80.9 
Khalkha            Tuvan              same family, 
different genera 
48 81.3 
Lithuanian         Russian            same family, 
different genera 
64 81.3 
Greek (Modern)     Bulgarian          same family, 
different genera 
64 81.3 
Khmer              Thai               different families 91 81.3 
Polish             Russian            same genus 71 81.7 
Russian            Serbian-Croatian   same genus 45 82.2 
Swahili            Zulu               same genus 107 82.2 
Dagur              Turkish            same family, 
different genera 
46 82.6 
Telugu             Kannada            same family, 
different genera 
47 83 
Kongo              Nkore-Kiga         same genus 48 83.3 
Dutch              German             same genus 56 83.9 
Italian            Spanish            same genus 63 84.1 
Drehu              Iaai               same genus 46 84.8 
English            Swedish            same genus 60 85 
French             Italian            same genus 64 85.9 
Hindi              Panjabi            same genus 49 91.8 
While Table 2 does not point in any specific direction and remains a curiosity, Table 3 provides 
fragments of information which fits into the larger picture that emerges from our study. We note 
that two pairs of unrelated languages, Vietnamese-Thai and Khmer-Thai, turn up in this list, which 
otherwise consists of genealogically unrelated language pairs. Furthermore, the rest of the pairs 
represent a mixture of languages related to different degrees (see Dryer 1992, 2005 for a definition 
of ‘genera’). 
 Returning to Figure 1 and the associated data in Table 1 let us proceed to overall 
interpretations. We set out asking whether there is some degree of similarity in typological profiles 
beyond which it is certain that languages are related. The answer is positive, but nevertheless 
discouraging. Members of language pairs in the sample that are 81.5% or more similar are all 
related. But only 12 pairs of languages are that similar, in spite of the fact that there are 1099 pairs 
of related languages in the sample! On the other hand, if there are less than 25% shared feature 
values all language pairs will be unrelated, and this goes for 545 pairs in the sample. If one allows 
for a very small margin of error (around 1%), it can predicted that less than 40% shared feature 
values implies unrelatedness. That goes for 12,034 language pairs in the sample—close to half of 
the total of 29,810. Thus, lack of similarity is a good predictor of unrelatedness, but presence of 
similarity is a bad predictor of relatedness. 
2. Are there ways of improving the results? 
We next consider the question of whether the prediction of relatedness could be improved 
somehow. In other studies (Holman et al. 2006a,b, Brown et al. 2006) we have made exact 
quantitative explorations of the relationship between typological similarity and geographical 
distance among languages. Not surprisingly, the greater the geographical proximity is between 
languages, the more similar they tend to be (this goes for both related and unrelated languages). If 
one takes into account the areal factor, this might move the cut-off point to allow more accurate 
predictions of relatedness. Testing this strategy was unsuccessful. We were not able to obtain 
markedly different results by adjusting the measure of similarity relative to geographical distance: 
the correlation between adjusted and unadjusted measures was 0.96. The reason for this is probably 
that the distance measure, as given in the WALS database, identifies the location of a given 
language (roughly) with its center of extension. This means that some neighbouring languages, such 
as German and Dutch, are treated as having a certain distance between them when in reality they 
don’t have any. The more widespread the languages compared are, the bigger this problem gets. 
Since it is impossible to provide adequate measure of geographical distances for 29,810 language 
pairs, and not just take recourse to a mechanical measure of distance from one WALS dot to 
another, it is not viable to improve on the cut-off point in such a way. 
 Also, the 134 features differ appreciably in the distribution of rarity and commonness 
among their values.  It is possible to imagine that taking into account the relative rarity of feature 
values might improve the predictions. We again failed to obtain markedly different results by 
adjusting the measure of similarity relative to differences among features: the correlation between 
adjusted and unadjusted measures was 0.98. The probable reason is that differences among features 
tend to average out in a sample of at least 45 attested features. 
   Another strategy to try to improve the power of prediction concerning relatedness 
would be to weight different features or values of features according to their stability. We have 
explored ways of measuring stability and have come out with a ranked order of stability for WALS 
features (Wichmann et al. 2006). Conceivably, if the features shared among languages were 
weighted for their stability the cut-off point could be pushed a bit. We expect, however, that the 
results would be similar to the results for taking into account rarity, since stable and unstable 
features would also average out. 
A final strategy to improve the results would be to take into account the areality of 
features. The linguistic typological literature abounds with statements concerning the susceptibility 
to diffusion of certain features as opposed to others. In practice, however, it turns out to be virtually 
impossible to define areas and measure areality in a consistent way. A major contribution of WALS 
has been to show that most typological features are ‘areal’ to various extents. Browsing the maps 
will make it clear to anyone that almost any feature can spread and that whatever features diffuse 
are the features that happen to exist in an area. Thus, ‘areality’ is not amenable to quantification in 
any straightforward way. 
3. Deviant language pairs 
The results reported on in Figure 1 and Table 1 show that there are a few pairs of languages which 
are related even though showing less than 40% similarities, which is the point where pairs tend 
overwhelmingly not to be related. It serves the record to provide a list of the pairs of related 
languages that are deviant in the sense that they show less similarities than related languages 
normally do. This list is provided in Table 4. 
Table 4. Related languages that have unusually different typological profiles (less than 40% 
similarities) 
Language A Language B Language family Number of 
features 
compared 
% Similarity 
Luvale             Ijo (Kolokuma)     Niger-Congo 52 28.8 
Zulu               Ijo (Kolokuma)     Niger-Congo 52 28.8 
Maidu 
(Northeast)  
Tsimshian 
(Coast)  
Penutian 48 29.2 
Ngiti              Koyra Chiini       Nilo-Saharan 47 29.8 
Yoruba             Ijo (Kolokuma)     Niger-Congo 51 31.4 
Mundari            Semelai            Austro-Asiatic 66 31.8 
Swahili            Ijo (Kolokuma)     Niger-Congo 50 32 
Maung              Yidiny             Australian 81 32.1 
Mundari            Khmer              Austro-Asiatic 78 32.1 
Koyraboro Senni    Murle              Nilo-Saharan 65 32.3 
Koromfe            Ijo (Kolokuma)     Niger-Congo 49 32.7 
Beja               Margi              Afro-Asiatic 45 33.3 
Sango              Ijo (Kolokuma)     Niger-Congo 51 33.3 
Nandi              Koyraboro Senni    Nilo-Saharan 47 34 
Nandi              Koyra Chiini       Nilo-Saharan 52 34.6 
Marathi            Spanish            Indo-European 52 34.6 
Margi              Amharic            Afro-Asiatic 49 34.7 
Mundari            Vietnamese         Austro-Asiatic 88 35.2 
Garo               Cantonese          Sino-Tibetan 51 35.3 
Berber (Middle 
Atlas) 
Kera               Afro-Asiatic 65 35.4 
Irish              Marathi            Indo-European 45 35.6 
Paamese            Acehnese           Austronesian 45 35.6 
Limbu              Mandarin           Sino-Tibetan 45 35.6 
Mandarin           Bawm               Sino-Tibetan 76 36.8 
Ijo (Kolokuma)     Diola-Fogny        Niger-Congo 46 37 
Ngiti              Nubian 
(Dongolese) 
Nilo-Saharan 54 37 
Miwok (Southern 
Sierra) 
Tsimshian 
(Coast)  
Penutian 62 37.1 
Mundari            Khmu'              Austro-Asiatic 70 37.1 
Bagirmi            Nubian 
(Dongolese) 
Nilo-Saharan 64 37.5 
Beja               Hausa              Afro-Asiatic 82 37.8 
Koromfe            Kisi               Niger-Congo 45 37.8 
Yidiny             Tiwi               Australian 90 37.8 
Limbu              Meithei            Sino-Tibetan 45 37.8 
Kera               Amharic            Afro-Asiatic 50 38 
Zulu               Yoruba             Niger-Congo 104 38.5 
Beja               Kera               Afro-Asiatic 57 38.6 
Ngiyambaa          Maranungku         Australian 74 39.2 
Malagasy           Acehnese           Austronesian 56 39.3 
Ngiti              Nandi              Nilo-Saharan 48 39.6 
Lugbara            Lango              Nilo-Saharan 53 39.6 
Fur                Ngiti              Nilo-Saharan 58 39.7 
Experts in the different families involved will surely have good explanations for these deviant 
cases. In some cases a pair may in reality not belong to the same family, as in the case of large and 
not altogether uncontroversial families such as Australian and Nilo-Saharan. In other cases, such as 
the two pairs featuring Marathi, a wide separation both temporally and geographically and 
interaction with widely different types of languages may conspire to make a related pair stand out as 
unusually different. In any case, measuring the amount of typological similarity provides a clue that 
‘something is going on’—either the classification is potentially wrong or heavy language contact is 
involved. So the method of comparing typological profiles is potentially useful for someone 
wishing to probe into the behavior of different languages within a proposed family. 
4. Conclusions 
The results reported on in this note were, in part, unsurprising and, in part, unexpected. Figure 1 
showed a close correlation between relatedness and typological similarity. This is what we had 
expected. But we also expected to find some minimal amount of typological similarity among 
language pairs which would suffice to predict that two languages are related. It turned out to be the 
case, however, that the amount of similarity required to make this prediction is so high (81.5%) that 
only few language pairs qualify. In practice, this means that typological features such as those of 
WALS are not useful for identifying relatedness among languages when it comes to comparisons of 
single pairs (when groups of languages are compared the situation may be different, but this issue is 
beyond the scope of this paper). At the other end of the scale we found that typological dissimilarity 
is a good predictor of unrelatedness: with only a small margin of error one can predict that 
languages which have 60% or more differences are not related according to the WALS 
classification. Our finding that a certain amount of typological differences can be used to  predict 
that languages are not commonly believed to be related means that typological differences are a 
yardstick for gauging the limits of the traditional comparative method. 
 While it was was not surprising to find a correlation between relatedness and the 
amount of typological differences among language pairs, this finding may nevertheless steer us in 
new directions. Presumably there is a correlation between the amount of shared basic vocabulary 
and relatedness as well. If so, the amount of shared basic vocabulary and the amount of typological 
similarity should also be correlated, and it may even be possible to start considering whether there 
is such a thing as a ‘typological clock’ such that the time of separation of languages of a given 
family may be inferred from the amount of typological differences within the family. The fact that 
unrelated languages may be as similar typologically as related ones indicates that for a ‘typological 
clock’ to work reasonably well, several pairwise comparison should be made. How, in practice, this 
kind of methodology could be developed would be an item for future research. 
References 
Brown, Cecil H., Eric W. Holman, Christian Schulze, Dietrich Stauffer, and Søren Wichmann. 
2006. Are similarities among languages of the Americas due to diffusion or 
inheritance? An exploration of the WALS evidence. Paper presented at the conference 
“Genes and Languages”, University of California Santa Barbara, September 8-10, 
2006. 
Dryer, Matthew S. 1992. The Greenbergian word order correlations.  Language 68:81-138. 
Dryer, Matthew S. 2005. “Genealogical language list,” in The World Atlas of Language Structures, 
edited by Martin Haspelmath, Matthew S. Dryer, David Gil, and Bernard Comrie, pp. 
584-643.  Oxford: Oxford University Press. 
Haspelmath, Martin, Matthew S. Dryer, David Gil, and Bernard Comrie (eds.). 2005. The World 
Atlas of Language Structures. Oxford: Oxford University Press. 
Holman, Eric W., Dietrich Stauffer, Christian Schulze, and Søren Wichmann. 2006. On the relation 
between structural diversity and geographical distance among languages: observations 
and computer simulations. (Revised version under review for Linguistic Typology). 
Holman, Eric W., Søren Wichmann, and Cecil H. Brown. 2006. Linguistic and cultural diffusion in 
a comparative perspective. Submitted. 
Wichmann, Søren, Eric W. Holman, & Hans-Jörg Bibiko. 2006. How computer simulations may 
help linguists: recent progress and prospects for more. Paper presented at the 
conference “Language and physics”, Warsaw, September 11-15, 2006.

  No abstract given; compares pairs of languages from World Atlas of Language
Structures.

Introduction
Being rare or ‘exotic’ is a relative phenomenon. From a Samoan point of view Burushaski is an 
extremely exotic language, but from the point of view of Telugu much less so. In this brief note we 
want to look a how different and how similar languages turn out to be in pairwise comparisons and 
the role that genealogical relatedness plays in this regard. We are interested in knowing whether 
there is a cut-off point Shigh in the amount of similarities such that we can be sure that language pairs 
that  have more than Shigh similarities are all generally thought to be related and also whether there 
is a cut-of point Slow at the other end of the scale such that all languages having less similarities than 
Slow are thought to be unrelated. In other words, if a language is ‘normal’ relative to some other 
language (as Burushaski is to Telugu), does this imply that the two languages are related according 
to commonly accepted classifications? Or, if two languages are mutually very exotic (as Burushaski 
and Samoan), does this imply that they are not thought to be related in commonly accepted 
classifications? 
 The data we use, as well as the genealogical classification, are from the World Atlas of 
Language Structures (Haspelmath et al., ed., henceforth WALS). The conclusions must of course be 
seen in relation to this particular dataset. Thus, when we observe a certain amount of typological 
similarity between two languages, this is strictly and only similarity in terms of the kinds of features 
investigated in WALS. The dataset includes 134 nonredundant features, each of which has from two 
to nine discrete values. All of these are quite generic typological features. Our conclusions are also 
limited to the amount of data available. We have required that for any language pair in our sample 
there should be 45 or more features attested for both members of the pair (a motivation for this 
precise number follows shortly). This has limited our sample to 320 languages and 29,810 pairs of 
languages compared. Among these pairs, there are 1,099 which are related according to the 
                                                           
 We would like to thank Bernard Comrie, Cecil Brown, and Dietrich Stauffer for comments on this manuscript. 
classification used in WALS. Henceforth we substitute ‘related’ for the more cumbersome ‘related 
according to the WALS classification’. We follow this classification because it seeks to meet a 
consensus view. 
1. Results 
Figure 1 presents the overall results of the investigation. As can be seen, the more similar languages 
get, the greater the probability is that they are related. The figures on which the curve is based are 
presented in Table 1. Percent similarity was defined as the percentage of attested features for which 
both languages have the same value. We have binned language pairs in 5% intervals from 10% to 
90% similarity. For the plot in Figure 1 the mean percent similarity in each interval was used. Table 
1 gives some additional information: it also shows how many language pairs belong in each 
interval. This is important for the interpretation of the results, as we shall see shortly. 
Before giving our interpretation let us explain why we have chosen the criterion that 
language pairs should have 45 or more features attested for both languages. It turns out that for a 
criterion of 30 or more features the curve is rather similar but not quite as steep, showing less 
dependence between the amount of similarity and the probability of finding related pairs. This 
indicates that the fewer features one operates with, the more prominent is random sampling 
variability in percent similarity. When operating with a criterion of 60 or more attested features the 
curve becomes uneven, indicating that the higher criterion passes too few pairs for stable results. 
This becomes even more pronounced when the criterion is 75 or more features. Obviously, with a 
more extended database the number of features taken to be criterial could be raised, but 45 is a 
number that suits the data available in WALS.  
Figure 1. The probability of finding related languages 
Probability of finding related languages
0 10 20 30 40 50 60 70 80 90 100
% similarity
Table 1. Data. (%SIM = % typological similarity between members of pairs; %REL = % of 
language pairs that are related; PAIRS = number of language pairs in range) 
%SIM %REL PAIRS 
10.0-14.9 0 11 
15.0-19.9 0 91 
20.0-24.9 0 443 
25.0-29.9 0.26 1566 
30.0-34.9 0.33 3904 
35.0-39.9 0.4 6019 
40.0-44.9 1.2 6772 
45.0-49.9 3.26 4873 
50.0-54.9 6.68 3520 
55.0-59.9 15.41 1551 
60.0-64.9 23.72 666 
65.0-69.9 38.24 238 
70.0-74.9 54.26 94 
75.0-79.9 61.54 39 
80.0-84.9 85 20 
85.0-89.9 100 2 
90.0-94.9 100 1 
 It may be of interest to mention the language pairs that fall in the lower and upper 
ranges of the percentage of shared values. Collectors of linguistic trivia may find it interesting that 
the members of the most divergent language pair in the world (in our dataset), i.e. Tümpisa 
Shoshone and Wari’, are found in the same general area, namely the Americas, that someone who is 
tired of Romance linguistics should turn to Nivkh and someone fed up with Swedish should visit 
the Koasatis when looking for something as radically different as it gets. Lists of the 20 most 
divergent language pairs and the 20 most similar ones are provided in tables 2 and 3. 
Table 2. The 20 most divergent language pairs in the sample 
Language A Language B Number of 
features 
compared 
% Similarity 
Tümpisa 
Shoshone   
Wari'              48 10.4 
Archi              Tukang Besi        46 13 
Maybrat            Limbu              45 13.3 
Italian            Nivkh              51 13.7 
Burushaski         Samoan             49 14.3 
Tzutujil           Burmese            49 14.3 
Ju|'hoan           Yup'ik (Central)   56 14.3 
Maybrat            Tamil              55 14.5 
Nubian 
(Dongolese) 
Acehnese           48 14.6 
Swedish            Koasati            47 14.9 
Klamath            Wari'              47 14.9 
Kongo              Ladakhi            46 15.2 
Bashkir            Maori              46 15.2 
Berber (Middle 
Atlas) 
Waorani            45 15.6 
Lango              Archi              45 15.6 
Archi              Thai               45 15.6 
Thai               Retuarã            45 15.6 
Ijo (Kolokuma)     Kutenai            50 16 
Kongo              Evenki             56 16.1 
Arabic (Egyptian)  Tümpisa 
Shoshone   
48 16.7 
Table 3. The 20 most similar language pairs in the sample 
Language A Language B Relatedness Number of 
features 
compared 
% Similarity 
Lango              Luo                same genus 46 80.4 
Luvale             Zulu               same genus 97 80.4 
Khmer              Vietnamese         same family, 
different genera 
89 80.9 
Vietnamese         Thai               different families 110 80.9 
Khalkha            Tuvan              same family, 
different genera 
48 81.3 
Lithuanian         Russian            same family, 
different genera 
64 81.3 
Greek (Modern)     Bulgarian          same family, 
different genera 
64 81.3 
Khmer              Thai               different families 91 81.3 
Polish             Russian            same genus 71 81.7 
Russian            Serbian-Croatian   same genus 45 82.2 
Swahili            Zulu               same genus 107 82.2 
Dagur              Turkish            same family, 
different genera 
46 82.6 
Telugu             Kannada            same family, 
different genera 
47 83 
Kongo              Nkore-Kiga         same genus 48 83.3 
Dutch              German             same genus 56 83.9 
Italian            Spanish            same genus 63 84.1 
Drehu              Iaai               same genus 46 84.8 
English            Swedish            same genus 60 85 
French             Italian            same genus 64 85.9 
Hindi              Panjabi            same genus 49 91.8 
While Table 2 does not point in any specific direction and remains a curiosity, Table 3 provides 
fragments of information which fits into the larger picture that emerges from our study. We note 
that two pairs of unrelated languages, Vietnamese-Thai and Khmer-Thai, turn up in this list, which 
otherwise consists of genealogically unrelated language pairs. Furthermore, the rest of the pairs 
represent a mixture of languages related to different degrees (see Dryer 1992, 2005 for a definition 
of ‘genera’). 
 Returning to Figure 1 and the associated data in Table 1 let us proceed to overall 
interpretations. We set out asking whether there is some degree of similarity in typological profiles 
beyond which it is certain that languages are related. The answer is positive, but nevertheless 
discouraging. Members of language pairs in the sample that are 81.5% or more similar are all 
related. But only 12 pairs of languages are that similar, in spite of the fact that there are 1099 pairs 
of related languages in the sample! On the other hand, if there are less than 25% shared feature 
values all language pairs will be unrelated, and this goes for 545 pairs in the sample. If one allows 
for a very small margin of error (around 1%), it can predicted that less than 40% shared feature 
values implies unrelatedness. That goes for 12,034 language pairs in the sample—close to half of 
the total of 29,810. Thus, lack of similarity is a good predictor of unrelatedness, but presence of 
similarity is a bad predictor of relatedness. 
2. Are there ways of improving the results? 
We next consider the question of whether the prediction of relatedness could be improved 
somehow. In other studies (Holman et al. 2006a,b, Brown et al. 2006) we have made exact 
quantitative explorations of the relationship between typological similarity and geographical 
distance among languages. Not surprisingly, the greater the geographical proximity is between 
languages, the more similar they tend to be (this goes for both related and unrelated languages). If 
one takes into account the areal factor, this might move the cut-off point to allow more accurate 
predictions of relatedness. Testing this strategy was unsuccessful. We were not able to obtain 
markedly different results by adjusting the measure of similarity relative to geographical distance: 
the correlation between adjusted and unadjusted measures was 0.96. The reason for this is probably 
that the distance measure, as given in the WALS database, identifies the location of a given 
language (roughly) with its center of extension. This means that some neighbouring languages, such 
as German and Dutch, are treated as having a certain distance between them when in reality they 
don’t have any. The more widespread the languages compared are, the bigger this problem gets. 
Since it is impossible to provide adequate measure of geographical distances for 29,810 language 
pairs, and not just take recourse to a mechanical measure of distance from one WALS dot to 
another, it is not viable to improve on the cut-off point in such a way. 
 Also, the 134 features differ appreciably in the distribution of rarity and commonness 
among their values.  It is possible to imagine that taking into account the relative rarity of feature 
values might improve the predictions. We again failed to obtain markedly different results by 
adjusting the measure of similarity relative to differences among features: the correlation between 
adjusted and unadjusted measures was 0.98. The probable reason is that differences among features 
tend to average out in a sample of at least 45 attested features. 
   Another strategy to try to improve the power of prediction concerning relatedness 
would be to weight different features or values of features according to their stability. We have 
explored ways of measuring stability and have come out with a ranked order of stability for WALS 
features (Wichmann et al. 2006). Conceivably, if the features shared among languages were 
weighted for their stability the cut-off point could be pushed a bit. We expect, however, that the 
results would be similar to the results for taking into account rarity, since stable and unstable 
features would also average out. 
A final strategy to improve the results would be to take into account the areality of 
features. The linguistic typological literature abounds with statements concerning the susceptibility 
to diffusion of certain features as opposed to others. In practice, however, it turns out to be virtually 
impossible to define areas and measure areality in a consistent way. A major contribution of WALS 
has been to show that most typological features are ‘areal’ to various extents. Browsing the maps 
will make it clear to anyone that almost any feature can spread and that whatever features diffuse 
are the features that happen to exist in an area. Thus, ‘areality’ is not amenable to quantification in 
any straightforward way. 
3. Deviant language pairs 
The results reported on in Figure 1 and Table 1 show that there are a few pairs of languages which 
are related even though showing less than 40% similarities, which is the point where pairs tend 
overwhelmingly not to be related. It serves the record to provide a list of the pairs of related 
languages that are deviant in the sense that they show less similarities than related languages 
normally do. This list is provided in Table 4. 
Table 4. Related languages that have unusually different typological profiles (less than 40% 
similarities) 
Language A Language B Language family Number of 
features 
compared 
% Similarity 
Luvale             Ijo (Kolokuma)     Niger-Congo 52 28.8 
Zulu               Ijo (Kolokuma)     Niger-Congo 52 28.8 
Maidu 
(Northeast)  
Tsimshian 
(Coast)  
Penutian 48 29.2 
Ngiti              Koyra Chiini       Nilo-Saharan 47 29.8 
Yoruba             Ijo (Kolokuma)     Niger-Congo 51 31.4 
Mundari            Semelai            Austro-Asiatic 66 31.8 
Swahili            Ijo (Kolokuma)     Niger-Congo 50 32 
Maung              Yidiny             Australian 81 32.1 
Mundari            Khmer              Austro-Asiatic 78 32.1 
Koyraboro Senni    Murle              Nilo-Saharan 65 32.3 
Koromfe            Ijo (Kolokuma)     Niger-Congo 49 32.7 
Beja               Margi              Afro-Asiatic 45 33.3 
Sango              Ijo (Kolokuma)     Niger-Congo 51 33.3 
Nandi              Koyraboro Senni    Nilo-Saharan 47 34 
Nandi              Koyra Chiini       Nilo-Saharan 52 34.6 
Marathi            Spanish            Indo-European 52 34.6 
Margi              Amharic            Afro-Asiatic 49 34.7 
Mundari            Vietnamese         Austro-Asiatic 88 35.2 
Garo               Cantonese          Sino-Tibetan 51 35.3 
Berber (Middle 
Atlas) 
Kera               Afro-Asiatic 65 35.4 
Irish              Marathi            Indo-European 45 35.6 
Paamese            Acehnese           Austronesian 45 35.6 
Limbu              Mandarin           Sino-Tibetan 45 35.6 
Mandarin           Bawm               Sino-Tibetan 76 36.8 
Ijo (Kolokuma)     Diola-Fogny        Niger-Congo 46 37 
Ngiti              Nubian 
(Dongolese) 
Nilo-Saharan 54 37 
Miwok (Southern 
Sierra) 
Tsimshian 
(Coast)  
Penutian 62 37.1 
Mundari            Khmu'              Austro-Asiatic 70 37.1 
Bagirmi            Nubian 
(Dongolese) 
Nilo-Saharan 64 37.5 
Beja               Hausa              Afro-Asiatic 82 37.8 
Koromfe            Kisi               Niger-Congo 45 37.8 
Yidiny             Tiwi               Australian 90 37.8 
Limbu              Meithei            Sino-Tibetan 45 37.8 
Kera               Amharic            Afro-Asiatic 50 38 
Zulu               Yoruba             Niger-Congo 104 38.5 
Beja               Kera               Afro-Asiatic 57 38.6 
Ngiyambaa          Maranungku         Australian 74 39.2 
Malagasy           Acehnese           Austronesian 56 39.3 
Ngiti              Nandi              Nilo-Saharan 48 39.6 
Lugbara            Lango              Nilo-Saharan 53 39.6 
Fur                Ngiti              Nilo-Saharan 58 39.7 
Experts in the different families involved will surely have good explanations for these deviant 
cases. In some cases a pair may in reality not belong to the same family, as in the case of large and 
not altogether uncontroversial families such as Australian and Nilo-Saharan. In other cases, such as 
the two pairs featuring Marathi, a wide separation both temporally and geographically and 
interaction with widely different types of languages may conspire to make a related pair stand out as 
unusually different. In any case, measuring the amount of typological similarity provides a clue that 
‘something is going on’—either the classification is potentially wrong or heavy language contact is 
involved. So the method of comparing typological profiles is potentially useful for someone 
wishing to probe into the behavior of different languages within a proposed family. 
4. Conclusions 
The results reported on in this note were, in part, unsurprising and, in part, unexpected. Figure 1 
showed a close correlation between relatedness and typological similarity. This is what we had 
expected. But we also expected to find some minimal amount of typological similarity among 
language pairs which would suffice to predict that two languages are related. It turned out to be the 
case, however, that the amount of similarity required to make this prediction is so high (81.5%) that 
only few language pairs qualify. In practice, this means that typological features such as those of 
WALS are not useful for identifying relatedness among languages when it comes to comparisons of 
single pairs (when groups of languages are compared the situation may be different, but this issue is 
beyond the scope of this paper). At the other end of the scale we found that typological dissimilarity 
is a good predictor of unrelatedness: with only a small margin of error one can predict that 
languages which have 60% or more differences are not related according to the WALS 
classification. Our finding that a certain amount of typological differences can be used to  predict 
that languages are not commonly believed to be related means that typological differences are a 
yardstick for gauging the limits of the traditional comparative method. 
 While it was was not surprising to find a correlation between relatedness and the 
amount of typological differences among language pairs, this finding may nevertheless steer us in 
new directions. Presumably there is a correlation between the amount of shared basic vocabulary 
and relatedness as well. If so, the amount of shared basic vocabulary and the amount of typological 
similarity should also be correlated, and it may even be possible to start considering whether there 
is such a thing as a ‘typological clock’ such that the time of separation of languages of a given 
family may be inferred from the amount of typological differences within the family. The fact that 
unrelated languages may be as similar typologically as related ones indicates that for a ‘typological 
clock’ to work reasonably well, several pairwise comparison should be made. How, in practice, this 
kind of methodology could be developed would be an item for future research. 
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