# understanding_and_exploiting_language_diversity__89b5fe2d.pdf Understanding and Exploiting Language Diversity Fausto Giunchiglia, Khuyagbaatar Batsuren, Gabor Bella DISI, University of Trento, Italy fausto@disi.unitn.it, k.batsuren@unitn.it, gabor.bella@unitn.it The main goal of this paper is to describe a general approach to the problem of understanding linguistic phenomena, as they appear in lexical semantics, through the analysis of large scale resources, while exploiting these results to improve the quality of the resources themselves. The main contributions are: the approach itself, a formal quantitative measure of language diversity; a set of formal quantitative measures of resource incompleteness and a large scale resource, called the Universal Knowledge Core (UKC) built following the methodology proposed. As a concrete example of an application, we provide an algorithm for distinguishing polysemes from homonyms, as stored in the UKC. 1 Introduction The problem of language diversity is very well known in the field of historical linguistics and has been studied for many years. Language diversity appears at many levels. Thus, on the level of phonology, while the use of consonants and vowels is a universal feature, the number and typology of these vary greatly across languages [Evans and Levinson, 2009], e.g., from the three vowels of some Arabic dialects to the 10 20 vowels of the English dialects. In morphology, at one end of the spectrum one finds analytic languages with very little to no intra-word grammatical structure, such as Chinese. In contrast, polysynthetic languages, e.g., some Native American languages [Evans and Sasse, 2002], have sentence-words that other languages would express through phrases or sentences [Crystal, 2004]. On the level of syntax, the various possible orderings of subject, verb, and object have been one of the earliest criteria in linguistic typology. Yet, it was shown that not even these three basic categories are truly universal [Aronoff and Rees-Miller, 2003]. This work has produced a large amount of relevant results with, however, limited practical usability, at least from an Artificial Intelligence (AI) perspective. There are at least two reasons why this has been the case. The first is that, even This work was supported by the ESSENCE Marie Curie Initial Training Network, funded by the European Commission s 7th Framework Programme under grant agreement no. 607062. when using statistical methods, this research has traditionally relied on low quantities of sample data, one main motivation being the difficulty of producing high quality large scale language resources. Large scale resources will always be very diversified across languages, more or less complete, more or less correct, more or less dependent on the subjective judgements and culture of the developers. The second is that this work has mainly focused on the syntactic aspects of diversity with much less attention to (lexical) semantics. Exemplar of the state of the art is the recent work in [Youn et al., 2016] which provides a quantitative method for extracting the universal structure of lexical semantics via an analysis of the polysemy of words. The study has been conducted on a data set of 22 concepts in 81 languages. At the same time, with the Web becoming global, the issue of understanding the impact of diversity on (lexical) semantics has become of paramount importance (see, e.g., the work on cross-lingual data integration [Bella et al., 2017] and the development of the large multilingual lexical resource Babel Net [Navigli and Ponzetto, 2010]). The successes in this area are undeniable, with still various unsolved issues. Thus, for instance, the Ethnologue project1, as of 2017, lists 7.097 registered languages while, to consider the most complete example, as from [Navigli and Ponzetto, 2010], Babel Net contains 271 languages. In this respect, it is worthwhile noticing that the languages of the so called WEIRD (Western, Educated, Industrial, Rich, Democratic) societies, namely most of the languages with better quality and more developed lexical resources, cannot in any way be taken as paradigmatic of the world s languages [Henrich et al., 2010], while many of the not so common minority languages, are disappearing from the Web with obvious long term consequences [Young, 2015]. The work described in this paper mutuates goals and means from both linguistics and AI. The main objective is to understand linguistic phenomena, as they appear in lexical semantics, through the analysis of large scale resources while exploiting these results to improve the quality of the resources themselves, with a special focus on minority languages. The proposed contribution improves the state of the art in AI, as it allows to develop better and better resources, but also in linguistics as it paves the way to large scale case studies. The 1http://www.ethnologue.com Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) main technical contributions are: 1. A formal quantitative measure of language diversity. Similar languages will tend to share certain phenomena while the same phenomenon shared by very diverse languages will be related to properties of the world rather than to the properties of single languages. This fact can be exploited to propagate properties across languages; 2. A set of formal quantitative measures of resource incompleteness. The incompleteness of lexical resources will always stay with us. The intuition is, therefore, to manage the bias it induces. Thus, within an experiment, the selection of a language will be mediated with its level of incompleteness in the features under consideration; 3. A general methodology for using the two measures defined above; 4. A large scale linguistic resource, called the Universal Knowledge Core (UKC), developed and used following the methodology proposed; 5. As a prototypical example of application, an algorithm for distinguishing polysemes from homonyms. Notice that we do not consider the issue of incorrectness, meaning by this the possibility that a word is given a (objectively recognized) wrong meaning. From how the UKC is built we assume that the percentage of mistakes is very low. Then the issue of incorrectness becomes an issue of interevaluator agreement, an issue for which we are content with any of the available alternatives. This is a consequence of a general assumption which underlies all our studies on how diversity appears in language and knowledge [Giunchiglia, 2006]. Following the approach taken by Millikan [Millikan, 2000] and Biosemantics in general, we see concepts as the result of an imperfect biological process, where there is no such thing as the ultimate representation of the world [Giunchiglia and Fumagalli, 2016]. We assume that, similarly to biological processes, language, like any other cultural phenomenon, e.g., music or architecture, changes across people and evolves in time (see also [Dawkins, 1976]). In this respect, linguistic resources are like any other data collected in biological experiments. We know they are always (partially) incorrect, the issue is how to handle this by putting in place the right data collection and measurement processes. This paper is organised as follows. Section 2 describes the key features of the UKC. Section 3 and 4 describe how we quantify language diversity and resource incompleteness. Section 5 describes the case study while Section 6 describes its main results. Finally, Section 7 presents the related work. 2 The Universal Knowledge Core We store linguistic data in a large scale multilingual knowledge base, called the Universal Knowledge Core (UKC). In the UKC the linguistic information is organized very similarly to Word Net [Miller et al., 1990]. Thus, we have words, synsets which store, for any word, its set of synonyms, senses which map words to synsets, glosses which are natural language descriptions of the intended meaning of the set of words in the corresponding synset, and examples which are Figure 1: The UKC structure. associated to glosses. Similarly to Babel Net, the UKC supports multiple languages while, similarly to Word Net, the UKC has an unique identifier associated to each synset. However, differently from Word Net and its derivatives,2 the UKC features a conceptual layer fully separated from language. In this layer, concepts are associated unique ids and are connected to language in one of three possible ways: (i) the concept id is mapped (one-to-one) to a synset id, which means that that concept is lexicalized in that language, (ii) the concept id is declared to be a lexical gap for that language, which means that that concept is not lexicalized in that language, and (iii) the concept id is not mapped, which means that we do not know what is the case. A new concept is added only if there is at least a language where it is lexicalized. Furthermore the usual lexico-semantic relations (e.g., hypernym, meronym) are embedded in the conceptual layer and connect concept ids, rather than synset ids. The conceptual layer is a kind of semantic layer (in model-theoretic terms, the domain of interpretation of the UKC lexicons) which provides a very powerful means for studying language diversity, while, at the same time, enabling language independent reasoning, as needed, for instance, in cross lingual and language independent applications [Giunchiglia et al., 2012a; Bella et al., 2017]. The overall organization of the UKC is represented in Fig.1. Here, the English word bike has two meanings, as verb and as noun, which are represented by two single word synsets which, through their reference concepts, are connected to the corresponding Italian words. In Italian we have a lexical gap as there is no word for the verb to bike. The two concepts, in turn, are connected in the graph of concepts. The UKC is in continuous evolution. It is populated via the import of freely available resources, e.g., Word Nets or dictionaries, which are preliminarily evaluated to satisfy certain minimal requirements of (very high) quality, or via user input [Giunchiglia et al., 2015]. Some relations in the UKC (e.g., 2See, for instance, http://globalwordnet.org. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) Table 1: Language Distribution. #Words #Languages Samples >90000 2 English, Finnish >75000 4 Mandarin, Japanese, etc. >50000 6 Thai, Polish, etc. >25000 17 Portuguese, Slovak, etc. >10000 29 Islandic, Arabic, et.c >5000 39 Swedish, Korean, etc. >1000 66 Hindi, Vietnam, etc. >500 85 Kazakh, Mongolian, etc. >0 335 Ewe, Abkhaz, etc. the fact that two senses are homonyms) are generated via reasoning tasks like the one described in this paper. The UKC contains only a very minor number of instances, differently from what is the case in Babel Net and in some applications of the UKC [Giunchiglia et al., 2012b], the main reason being our interest in studying language as such, without cluttering it with billions of instances. As a matter of fact, most of the instances present in Word Net have been removed. As of today, the UKC contains 335 languages, 1,333,869 words, 2,066,843 senses, and more than 120,000 concepts where, as it should be expected, no concept is lexicalized in all languages. Table 1 reports the distribution of words over languages. Notice that 90% of the words belong to 50 languages, and that 60% of the languages belong to three phyla (i.e., groups of languages related to one another but less closely than in a family), namely: 115 languages (e.g., Italian) to the Indo European phylum, 52 languages (e.g., Mongolian) to the Ural-Altaic phylum and 36 languages (e.g., Malay) to the Austronesian phylum. 3 Quantifying Language Diversity The problem of quantifying the diversity of languages is not new, see, e.g., [Bell, 1978; Youn et al., 2016]. Our ideas build upon the work described in [Rijkhoff et al., 1993]. The main goal of this work was to construct balanced datasets with the goal of avoiding linguistic bias. Still sharing the same intuitions, we work in the other direction. Namely, we have the data sets and we measure their diversity in order to exploit it in the solution of well-known linguistic problems. Diversity has many causes. To name some: genetic ancestry (languages with common origins), geography (due to the influence of physical closeness), culture (effects of cultural dominance). In this paper we present a first attempt at quantifying a global combined diversity measure in terms of genetic diversity and geographic diversity. Given a language set L, we define its combined diversity measure as follows: Com Div(L) = Gen Div(L) + βGeo Div(L) (1) In the equation above β [0, 1] normalizes the effects of genetic diversity over those of geographic diversity. We compute the Relative (Combined) Diversity of two languages by taking |L| = 2 and we (generically) say that two or more languages are similar when they are not diverse and we extend this terminology to all forms of diversity. Let us define the notions of genetic and geographic diversity. Figure 2: A fragment of the phylogenetic tree. Languages are organized in a Language Family Tree which represents how, in time, languages have descended from other languages, starting from the ancestral languages [Bell, 1978]. A fragment of this tree is shown in Fig.2. This figure must be read as follows. The root is a placeholder for collecting all languages. Labeled intermediate nodes are sets of languages (phyla or families) where the label is the name of the set. Unlabeled intermediate nodes correspond to missing names of language sets and serve the purpose of keeping the tree balanced (crucial for the computation of diversity, see below). Leaves denote languages. In general, we write T (L) to mean the family tree T for the set of languages L (when clear we drop the argument from T ). The idea behind the computation of genetic diversity is that languages that split closer to the root (that is, further back in time) will have more fundamental changes than those involved in the more recent splits. We capture this intuition by pondering each node n in the Language Family Tree by a real number that decreases with the distance from the root. Thus languages which split very early will generate multiple long branches, thus increasing the overall diversity value. While [Rijkhoff et al., 1993] used linearly decreasing weights, we have chosen the inverse exponential of λ depth(n) where the depth of the Root is 0 and thus its weight is 1 and where, below it, each phylum is weighted 1/λ, then 1/λ2, and so on. Furthermore we normalize Gen Div to be in the range [0,1]. More specifically, let T (E) be the family tree of a reference set of languages E, which in our case we take to be the languages in the UKC. Let L E be a set of languages for which we want to compute the diversity level and T (L) the corresponding minimal subtree of E. Then, the genetic diversity of L is taken to be 0 if |L| < 2, and, otherwise, defined as: Abs Gen Div(L) = X n T λ depth(n) 1 (2) Gen Div(L) = Abs Gen Div(L) Abs Gen Div(E) (3) where Abs Gen Div is what we call the Absolute Genetic Diversity and Abs Gen Div(E) is the Reference Genetic Diversity. To provide some examples, assume we take λ = 2. Then Abs Gen Div(E) = 88.127 and Gen Div(E) = 1, while, with L1 ={Hungarian, Italian, Polish, Russian, Basque} (the languages in Fig. 2) we have Abs Gen Div(L1) = 3.469 and Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) Gen Div(L1) = 0.039. Similarly, if we consider a less diverse subset including only Indo-European languages, e.g., L2 ={Italian, Polish, Russian} we have Abs Gen Div(L2) = 1.531 and Gen Div(L2) = 0.017. In this latter case, adding other Romance languages, e.g., Spanish, Catalan, and Portuguese, to L2 would increase Gen Div only to 0.022, The definition of geographic diversity captures the intuition that languages with speakers living closely to one another tend to share more features and, in particular, a larger portion of their lexicon. This can be explained both diachronically (by the co-evolution of languages) and synchronically (these people will deal with the same types of objects and phenomena). As a first approximation, given that the UKC contains languages from everywhere in the world, we capture this intuition by defining our geographic diversity measure based on the number of different continents on which the languages in the reference data set are spoken. Then, the geographic diversity of L is taken to be 0 if |L| < 2, and, otherwise, defined as: Geo Div(L) = | S l L continent Of(l)| #Continents (4) where continent Of(l) is the continent where l is spoken. It is important to notice that the computation of geographic diversity through distance metrics alone is a gross oversimplification. Topology and the roughness of terrain, for instance, are important factors: mountain-dwelling people from geographically nearby valleys may in reality be completely isolated from each other. Historical periods of proximity are also ignored by synchronic only approaches, e.g., the temporary mixing of tribes having migrated together through the Eurasian Steppe to then settle at great distances from each other. Still, at this stage, the values of diversity we compute are good enough to produce interesting results. 4 Quantifying Resource Incompleteness We define two types of incompleteness, i.e., language incompleteness, concept incompleteness and their corresponding measures of coverage plus the notion of ambiguity coverage. The notion of language incompleteness is a direct extension of the notion of incompleteness of logical languages and theories. Given a reference domain of interpretation, in our case the set of concepts, language incompleteness measures how much of it cannot be named by the elements of the language. We have the following: Abs Lan Cov(l) = |Concepts(l)| (5) Lan Cov(l) = |Abs Lan Cov(l)| |Concepts(UKC)| |Gaps(l)| (6) Lan Inc(l) = 1 Lan Cov(l) (7) where Concepts(l) is the set of concepts denoted by the words in l and Concepts(UKC) is the set of concepts in the UKC (i.e., the concepts denoted by the languages in the UKC). |Concepts(UKC) is decreased by |Gaps(l)|, namely the number of lexical gaps in l to take into account the fact different languages describe different worlds. We call Abs Lan Cov the Absolute Language Coverage. Table 2 (left) organizes the languages of the UKC into four groups, (a), (b), (c), (d), with the first two being highly developed and the latter two being highly under-developed. The notion of concept incompleteness can be thought of as the dual of language incompleteness. If the latter measures how much of the UKC a language does not cover, the former measures how much a single concept is covered across a selected set of languages. Let, for any concept c, the Languages of c be the set of languages where c is lexicalized, defined as: Languages(c) = [ l L {l|σ(c, l) > 0} (8) where σ(c, l) returns either 1 or 0, depending on whether c is lexicalized in l. Then we define concept coverage and of concept incompleteness as follows: Abs Con Cov(c) = |Languages(c)| (9) Con Cov(c) = Abs Con Cov(c) |Languages(UKC)| (10) Con Inc(c) = 1 Con Cov(c) (11) In words: the absolute coverage of a concept is the cardinality of the set of languages where it occurs, its coverage is the absolute coverage normalized over the number of languages of the UKC (defined as Languages(UKC) with a slight abuse of notation), its incompleteness is the complement to 1 of its coverage. Figure 3 shows the distribution of concepts for each value of Abs Cont Cov(Concept) with Concept standing for the sets of the concepts corresponding to the four parts of speech (i.e., adjective, adverb, noun, and verb). As it can be seen from the mean line, on average, concepts are lexicalised across about 10.99 languages. Figure 3: Concept distributions per Abs Con Cov value. As it is well known, the key difference between logical languages and natural languages is that the latter, differently from the former, allow words to denote more than one concept. The occurrence of multiple concepts denoted by the same word gives rise to the phenomenon of lexical ambiguity, e.g., polisemy or homonymy. Let the 4-tuple a = be an ambiguity instance for l, where c1 and c2 are two concepts expressed by the same word w in the language l. We define the notion of ambiguity coverage as: Amb Cov(a) = |Languages(c1) Languages(c2)| (12) The ambiguity coverage of an ambiguity instance measures the level of lexicalization of its concepts in the UKC. The higher this value is the more evidence we have, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) Table 2: Language Groups. Groups Language Incompleteness #Words #Languages Sample Languages #Amb Ins Avg Amb Cov a Lan Inc(l) [0.00; 0.52[ W [50, 001; + ] 6 English, Finnish, ... 714,437 10.2 b Lan Inc(l) [0.52; 0.82[ W [20, 001; 50, 000] 15 Dutch, Spanish, ... 1,969,436 12.8 c Lan Inc(l) [0.83; 0.99[ W [501; 20, 000] 64 Danish, Albanian, ... 117,213 18.4 d Lan Inc(l) [0.99; 1.00] W [1; 500] 250 Ewe, Abakhaz, ... 1,725 35.5 UKC Lan Inc(l) [0.00; 1.00] W [1; + ] 335 2,802,811 12.4 across languages, towards establishing the type of ambiguity. Amb Cov(a) gives us the coverage of a single instance. However, in order to have an overall coverage measure we need to compute, for a given set of languages L, the overall set of ambiguity instances Amb Ins(L) and the average ambiguity coverage Avg Amb Cov(L), namely the average coverage of instances over the languages in L. We have the following: Amb Ins(L) = [ a l {a|a =< l, w, c1, c2 >} (13) Avg Amb Cov(L) = a Amb Ins(L) Amb Cov(a) |Amb Ins(L)| (14) In other words, we compute Amb Ins(L) by collecting all instances across all languages in L and Avg Amb Cov(L) by summing the average coverage of all instances and then by dividing it by the number of these same instances. Table 2 (right) reports the number of ambiguity instances and their average number for the four language groups plus the UKC. Notice how the average absolute ambiguity coverage is much higher for the under-developed language groups (c), (d). In other words language coverage increases when the average ambiguity coverage decreases, and vice versa: the more developed a resource is the less ambiguity instances we have. This fact, counter-intuitive at first sight, is most probably a consequence of the fact that, in practice, the first words added to a language are the ones which are most commonly used and therefore, the most ambiguous. 5 Polysemy vs. Homonymy The issue of Lexical Semantic Relatedness has been extensively studied, see, e.g., [Budanitsky and Hirst, 2006]. However, all the work so far has mainly, if not exclusively, concentrated on its study within a single language while we focus on how semantic relatedness propagates across languages. To get an insight into the problem, consider the three examples in Tables 3, 4, 5. These tables provide examples of the types of semantic relatedness we consider. Notice that we distinguish between two types of morphological relatedness: compounding,3, namely the combination of free morphemes (as in key + board keyboard), and derivation namely the combination of a word with one or more derivational affixes (bound morphemes) (as in play + -er player). 3We use the term compounding to cover also idioms and collocations where component words are separated by spaces: hot dog, tax cut. This is justified by the fact that the presence or absence of spaces is more a matter of language-specific orthographical convention than a semantic differentiator (e.g., English prefers multiword expressons, German tends to use compounding, whereas some languages such as Chinese do not use spaces to separate words at all). Table 3: An example of polysemy in English. # Language Concept 1 Concept 2 Types 1 English bar bar polyseme 2 Italian barra bar derivational 3 Mongolian different 4 Chinese 酒吧 酒馆 derivational . . . . . . . . . . . . ... 23 Finnish baaritiski baari compound Types polyseme compound derivational different Languages 11 1 5 6 Concept 1: a counter where you can obtain food or drink. Concept 2: an establishment where alcoholic drinks are served over a counter. Table 4: An example of homonymy in English. # Language Concept 1 Concept 2 Types 1 English melody, air air homonym 2 Italian melodia, aria aria homonym 3 Mongolian different 4 Chinese 旋律 空气 different . . . . . . . . . . . . ... 38 Turkish melodi hava different Types homonym compound derivational different Languages 6 0 0 32 Concept 1: a succession of notes forming a distinctive sequence. Concept 2: a mixture of gases (especially oxygen) required for breathing. The key observation is that diverse languages represent the same semantic relatedness in diverse ways. Thus, for instance, in Table 3, a polyseme in English corresponds to an occurrence of derivational morphology in Italian and Chinese, to an occurrence of compound morphology in Finnish and to two distinct words in Mongolian. Our goal is to establish whether any two concepts denoted by a single word are polysemes of homonyms. The algorithm we propose is based on the following intuitions: if two concepts are semantically related in diverse languages, then they are polysemes. In this case the diversity of the two languages is evidence of the fact that semantic relatedness derives from a property of the world, which is what all languages denote. if two concepts are not semantically related in diverse languages, then they are homonyms. The key idea is that the occurrence of a homonym in a single language, or in similar languages is a coincidence, a consequence of some local, e.g., contextual or cultural, phenomena. Similar languages provide little support for the discovery of polysemes and homonyms. At the same time, the existence of polysemes and homonyms can be propagated across similar languages. But, how do we automatically recognize that two concepts are semantically related? The idea is simple: if we have a big enough number of diverse languages where the two words denoting the two concepts are syntactically similar, then the two concepts are semantically related. A consistent use of the Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) Table 5: An example of compound morphology in English. # Language Concept 1 Concept 2 Types 1 English tennis tennis player compound 2 Italian tennist tennista derivational 3 Mongolian derivational 4 Chinese 网球 网球选手 compound ... ... ... ... . . . 25 Korean ᄐ ᅦᄂ ᅵᄉ ᅳ ᄐ ᅦᄂ ᅵᄉ ᅳᄉ ᅥ ᆫᄉ ᅮ compound Types polysemy compound derivational different Languages 0 11 14 0 Concept 1: a game played with rackets by two or four players who hit a ball back and forth over a net that divides the court. Concept 2: an athlete who plays tennis. similar words is evidence of semantic relatedness, as it also the case in the examples in Tables 3, 5. The resulting algorithm (see algorithm 1) takes in input an ambiguity instance x and a multilingual resource and it returns one of three classifications for x: polyseme, homonym or unclassified. This algorithm is structured as follows: Step 1. (Lines 1-2). It initializes the set LP of the languages supporting the occurrence of a polyseme (Line 1) and it collects in L all the languages where c1 and c2 are lexicalized (Line 2); Step 2. (Lines 3-7). It tries to recognize x as a candidate polyseme. This attempt succeeds if one of two conditions hold: (i) the two words are the same, i.e., we have discovered another case of polisemy in a new language or (ii) the two words are morphologically related, as computed by the function morph Sim. If it succeeds it adds l to LP . morph Sim(w1, w2) = len(LCA(w1, w2)) max(len(w1), len(w2))) (15) Our current implementation of morph Sim, is a (quite primitive) string similarity metric. For w1 and w2 to be related, morph Sim(w1, w2) must return a value higher than a threshold TM. The function len() returns the length of its input while the function LCA() returns the longest common affix (prefix or suffix) of the two input words: for example, compet is the LCA for the words compete and competition . Step 3. (Line 8) It creates the set LH of the languages supporting the occurrence of a homonym. Notice how LH contains the languages where w1 and w2 are different words. Step 4. (Lines 9-14) x is classified. Notice that, for x, to be classified as a polyseme, the combined diversity of LP must be higher than TD (where D stands for Diversity) while, to be classified as a homynm, the combined diversity of LH must be higher than TD and lower than TS (where S stands for Similarity). We call TD and TS the Diversity Threshold and the Similarity Threshold, respectively. The intuition is that an ambiguity instance is a polyseme if it occurs in a diverse enough language set while it is a homonym if it occurs in a language set where the languages supporting homonymy are diverse enough and the languages supporting polisemy are similar enough . One such example are the two homonyms, one in English and one in Italian, in Table 4. We organize this section in three parts. First we describe how we have learned the hyperparameters. Then we describe the Algorithm 1: Lexical Ambiguity Classification Input : x =, an ambiguity instance Input : R, a multilingual lexical resource Output : label, an ambiguity class for the instance a. 2 L Languages R(c1) Languages R(c2); 3 for each language l L do 4 for each word w1 Words R(c1, l) do 5 for each word w2 Words R(c2, l) do 6 if w1 = w2 or morph Sim(w1, w2) then 7 LP LP {l} ; 8 LH L LP ; 9 if Com Div(LP ) >TD then 10 label polyseme ; 11 else if Com Div(LH) >TD and Com Div(LP ) 0, > 10, > 20, ...) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) Figure 4: Classification results vs. required minimal number of ambiguity instances. Table 9: UKC classification results from Figure 4. UKC Classification Results Amb Cov #Amb Ins Polyseme% Homonymy% Unclassified% >0 2,802,811 32.4 21.2 46.4 >10 1,805,144 41.9 14.4 43.5 >20 325,322 55.3 11.6 32.9 >30 44,408 64.2 11.0 24.7 >40 9,556 71.5 10.2 18.1 >50 3,198 73.7 10.9 15.3 which are required for accepting an ambiguity instance as such, impacts the classification results. It shows how, for all the language groups, with the growth of the minimal number of required ambiguity instances, the proportion of homonyms tends to converge to a low percentage (below the 20%), while the proportion of polysemes tends to converge to a very high percentage (above the 70%), and the proportion of unclassified instances decreases substantially (below the 20%). This is coherent with our expectation of a very low percentage of honymyms, most likely below the 10%. Table 9 provides the numeric quantification of the UKC results graphically represented in Figure 4, together with the extra information of the number of instances computed. It can be noticed how increasing the minimal required number of ambiguity instances consistently increases the percentage of polysemes (up to the 73.7%), decreases the percentage of homonyms (down to the 10.9%) as well as the percentage of unclassified instances (down to around the 15.3%) Table 10 refines the results in Table 9 by showing how the accuracy with polysemes and homonyms grows with the growth of Amb Cov, namely with the growth of the number of languages where the two concepts occurring in an ambiguity instance are lexicalized. It can be seen the accuracy of polysemy is very robust while that of homonymy is highly sensitive to the number of languages, converging to high levels of accuracy. Table 10: Classification accuracy vs. ambiguity coverage. Accuracy% Amb Cov #Polysemes #Homonyms Total Hom.% Pol.% >0 334 306 640 52.2 98.3 >10 267 297 564 52.9 98.5 >20 173 143 316 60.1 98.8 >30 103 33 136 69.7 99.0 >40 56 10 66 70.0 98.2 >50 30 7 37 71.4 100.0 7 Related Work The universality of linguistic phenomena has been in the focus of historical and comparative linguistics, as well as of the related field of linguistic typology [Croft, 2002]. Universality has been most famously researched on the syntactic level in search of a universal grammar [Evans and Levinson, 2009] but also in the lexicon. Classic quantitative approaches as described in [Mc Mahon and Mc Mahon, 2005], such as lexicostatistics [Swadesh, 1955], mass comparison [Greenberg, 1966], or the recent paper [Youn et al., 2016] on the universality of semantic networks, perform comparisons on relatively small (of up to a couple hundred entries) but very carefully selected word lists expressing the same meaning across a large and unbiased language sample (e.g., the Swadesh list [Swadesh, 1971]). Our research, on the contrary, takes the results of experts on genetic relationships as granted for our diversity measures. Beyond understanding the diversity of the language sets we are working on and thus evaluating the scope of cross-lingual applicability of our results we have no a priori reason to exclude certain types of words or phenomena from our experiments and can leverage entire lexicons available to us. The intuition is that the scale of the resource will average out local biases. The study of polysemy also has a long history, see, e.g., [Apresjan, 1974; Lyons, 1977]. In particular, various computational methods have been proposed for the prediction and generation of polysemy instances from regular (productive) patterns [Buitelaar, 1998; Peters, 2003; Srinivasan and Rabagliati, 2015; Freihat et al., 2016]. Our study goes beyond the limitation of regularity as our goal is not to create rules to be applied over classes of concepts but, rather to find widely recurring polysemy patterns across multiple languages with respect to specific concept pairs. 8 Conclusion In this paper we have presented a general approach which allows us to use large scale resources, in our case, the UKC, for the solution of relevant language related problems and use the results to improve the UKC itself. The proposed approach has been applied to the discovery of homonyms, as distinct from polysemes, in the UKC. 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