Measuring Prefixation and Suffixation in the Languages of the World (2021)

Abstract: It has long been recognized that suffixing is more common than prefixing in the languages of the world. More detailed statistics on this tendency are needed to sharpen proposed explanations for this tendency. The classic approach to gathering data on the prefix/suffix preference is for a human to read grammatical descriptions (948 languages), which is time-consuming and involves discretization judgments. In this paper we explore two machine-driven approaches for prefix and suffix statistics which are crude approximations, but have advantages in terms of time and replicability. The first simply searches a large collection of grammatical descriptions for occurrences of the terms ‘prefix’ and ‘suffix’ (4 287 languages). The second counts substrings from raw text data in a way indirectly reflecting prefixation and suffixation (1 030 languages, using New Testament translations). The three approaches largely agree in their measurements but there are important theoretical and practical differences. In all measurements, there is an overall preference for suffixation, albeit only slightly, at ratios ranging between 0.51 and 0.68.

## introduction it has long been recognized that suffixing is more common than prefixing in the languages of the world (see @xcite . more detailed statistics on this tendency are needed to sharpen and evaluate proposed explanations for this tendency. in particular, dense data is needed to properly account for genealogical and areal effects (cf. @xcite . with some 7 000 languages in the world, gathering these data is a gargantuan task. in this paper, we investigate three approaches that span the range from minimal to maximal curation. motivated by potential functional explanations @xcite , the ideal measure for prefixing/suffixing would be to count the proportion of prefixes/suffixes per phonological word in a morphologically segmented corpus (cf. @xcite . it is believed that such ratios converge as the corpus grows towards infinite amounts of sampled data produced by the speakers of a language, and as such the ratios constitute properties of the language. the ideal measure would range from 0 to (potentially) infinity but, in practice, ratios beyond 5 are unheard of. an alternative equivalent characterization is to have an affixation score (as) from 0 to (potentially) infinity comprising both prefixes and suffixes, along with a ratiocalled the suffix ratio (sr) -from 0.0 to 1.0 of the division of labour between suffixes and prefixes ( s s+p ). we use this characterization here, remembering that it is only defined for languages which have at least some affixation. since large morphologically segmented corpora are not available for a wide range of languages of the world, the ideal token count measure must be approximated. the classic approach, which we may call humans read grammars (hrg), is for a human to extract the relevant information from grammatical descriptions of the languages of the world. this approach is ideal in many ways, but requires a large amount of manual labour and requires a certain amount of judiciousness on behalf of the curator. while grammars are systematizations of raw text/spoken data, they rarely contain token counts, so this approach can only reflect any specific ratios indirectly. at the other end of the spectrum, a quick-and-dirty approach where machines read grammars (mrg) is possible now that large collections of digitized grammatical descriptions are available and practical to use. we may obtain a crude approximation of the functional load of prefixes/suffixes by simply counting the occurrences of the terms prefix and suffix of the same grammatical descriptions that were written for a human audience. while there are obvious drawbacks to such a "naive" measure, it has obvi-ous advantages in terms of speed, replicability and transparency. a similar crude measure may also be obtained by machines read raw text (mrt) given that a large collection of (not infinite-size, but comparable) raw text collection of new testaments are available electronically @xcite . correct automatic morphological segmentation and labeling of such a large array languages is not possible at present. nevertheless, measures inspired by work in unsupervised learning of morphology @xcite ) may be enough to gauge the amount and ratio of affixation even if the tokens cannot be accurately segmented. ## related work currently the largest available humanly curated database on prefixation/suffixation in the languages of the world is the wals chapter 26a by @xcite featuring 948 languages. it continues a long tradition of growing databases of similar kinds (see, e.g., @xcite . we use the @xcite database here as it represents the culmination of these efforts and is available and methodologically explicit. information extraction from grammatical descriptions has only recently become possible in practice, with the advent of a large collection of digitized grammars @xcite . given its novelty, only a few embryonic approaches @xcite @xcite @xcite have addressed the task so far. arguably, the task in the present study is keyword-associated (of the simplest kind) wherefore we follow the method of @xcite which requires no tuning of parameters and estimates a noise-level for each source in addition to the simple counts. while there are no comparable morphologically segmented corpora for a wide range of languages, it should be noted that there is a growing body of scattered resources in the nlp world (e.g., @xcite , morphologically segmented texts in the dobes and elar archives (e.g., paschen et al. 2020), and interlinear glossed text extracted from miscellaneous publications (see references cited in round et al. 2020 and howell 2020). these resources do not yet have the breadth and comparability required for the present study, but the large raw text parallel bible corpus of mccarthy et al. (2020) does -the culmination of a several decades long tradition of amassing bible corpora for nlp. combined with unsupervised morphological segmentation they could provide an excellent resource for direct measurements of affixation. a very large body of work in unsupervised learning of morphology (see @xcite @xcite for an overview of more recent work) seeks to do segmentation of raw text. however, despite some progress to date, no off-the-shelf method exists that will segment a very broad range of languages accurately without a large amount of manual tuning of parameters, if even then. fortunately, for the present task, we only need a score reflecting affixation, not necessarily an accurate segmentation itself. we have thus chosen one of the simplest counting techniques for overrepresentation of initial/terminal string segments (cf. @xcite explained in section 3.3, thought to reflect actual segmentation proportionately. many other choices would have been possible, with, we suspect, largely equivalent outcomes. ## conclusion we have compared three ways to obtain data on the amount of prefixes/suffixes in the languages of the world. the three measures, correlate to a high degree but none can be said to reflect an ideal measure. at the same time, there are considerable differences in the measurements of individual languages. these differences reflect differences in aim and scope as well as sketchy measurements. the humans read grammars method only focusses on inflectional morphology with only weak integration of functional load. the machines read grammars approach is vulnerable to differences in scope of description and individual styles, of which there is plenty of variation for the same language. more research is needed in to see to what extent these dimensions of variation can somehow be normalized automatically. the machines read raw text method reads a very noisy reflection of prefixation/suffixing from the raw data and cannot differentiate between derivational, inflectional or fossilized/productive affixation. the simple measure used here should be abandoned in favour of a more complicated, but less noisy measure. the resulting database, in total spanning the tremendous 4 437 languages, is freely available for future research at zenodo http://doi.org/10. 5281/zenodo.4731249 on a creative commons attribution 4.0 international license.