Inferring Morphological Complexity from Syntactic Dependency Networks: A Test (2021)

Abstract: Research in linguistic typology has shown that languages do not fall into the neat morphological types (synthetic vs. analytic) postulated in the 19th century. Instead, analytic and synthetic must be viewed as two poles of a continuum and languages may show a mix analytic and synthetic strategies to different degrees. Unfortunately, empirical studies that offer a more fine-grained morphological classification of languages based on these parameters remain few. In this paper, we build upon previous research by Liu & Xu (2011) and investigate the possibility of inferring information on morphological complexity from syntactic dependency networks.

## introduction language classification based on morphological profiles has prominently featured in the linguistic typology research agenda since the earliest days of the discipline. earlier 19 th century classifications essentially focused on morphological complexity in terms of the number of morphemes per word and the number of meanings per morpheme, and proposed that languages may be typologized into neatly discrete type, e.g. 'isolating', 'agglutinative', 'inflectional' (see @xcite . 1 however, it soon became clear that such a holistic approach does not adequately capture the variation of natural languages (already @xcite . instead, morphological complexity should be viewed as an empirically measurable "multidimensional typological space" @xcite , in which languages can be arranged based on a number of parameters. 2 based on this line of reasoning, scholars have variously tried to measure morphological complexity by means of quantitative methods and classify languages accordingly. in this paper, we build upon a proposal by @xcite and investigate whether syntactic dependency networks can be effectively used as tools for measuring (at least some aspects of) morphological complexity. the paper is structured as follows: in section 2 we review previous research on quantitative approaches to morphological typology. section 3 briefly introduces syntactic dependency networks and network analysis. section 4 is devoted to our own analysis. we first illustrate our data and methods (section 4.1 and 4.2), and then present and discuss our results (section 4.3 and 4.4). section 5 contains a summary of our findings. inferring morphological complexity from syntactic dependency networks: a test ## syntactic dependency networks in this section, we describe syntactic dependency networks and their properties (section 3.1), and we illustrate various indexes that can be used to interpret network structure (section 3.2), with a focus on those indexes that we use in our own analysis in section 4. ## conclusions in this paper, we have put to an empirical test the proposal advanced by @xcite that syntactic dependency networks can be exploited to investigate cross-linguistic variation in morphological complexity. our findings only partly support the validity of this methodology. while we are sympathetic with the underlying assumptions, we must conclude, against @xcite more optimistic view, that when applied to larger cross-linguistic datasets, network indexes do not yet yield consistently interpretable results as to morphological complexity. this means that more research is needed to fully ascertain the suitability of networks to explore morphological complexity. in particular, more attention needs to be paid to the role of treebank size and to the potential impact of annotation schemas. another potentially confounding factor is that we have worked on networks directly extracted from treebanks as a whole. it needs to be tested whether better results may be achieved by working with networks that operate a finer-grained distinction for e.g. parts of speech. finally, we must stress that even for neat data such as that in figure 5 , the proposed correlation between network indexes and the language's analyticity index must remain at this stage tentative. while there might well be a linguistic motivation to link higher number of nodes and average path length to higher syntheticity, the validity of these assumptions needs to be tested against a finergrained qualitative assessment such as that variation in paradigm size in inflectional languages can also be explored with networks, by comparing word-based with corresponding lemma-based networks (see @xcite .