Chunshan Xu

Dependency distance: A new perspective on syntactic patterns in natural languages

Dependency distance, measured by the linear distance between two syntactically related words in a sentence, is generally held as an important index of memory burden and an indicator of syntactic difficulty. Since this constraint of memory is common for all human beings, there may well be a universal preference for dependency distance minimization (DDM) for the sake of reducing memory burden. This human-driven language universal is supported by big data analyses of various corpora that consistently report shorter overall dependency distance in natural languages than in artificial random languages and long-tailed distributions featuring a majority of short dependencies and a minority of long ones. Human languages, as complex systems, seem to have evolved to come up with diverse syntactic patterns under the universal pressure for dependency distance minimization. However, there always exist a small number of long-distance dependencies in natural languages, which may reflect some other biological or functional constraints. Language system may adapt itself to these sporadic long-distance dependencies. It is these universal constraints that have shaped such a rich diversity of syntactic patterns in human languages.

Can syntactic networks indicate morphological complexity of a language

In this study, the complex-network approaches are employed to investigate the word form networks and the lemma networks extracted from dependency syntactic treebanks of fifteen different languages. The results show that it is possible to classify human languages by means of the main parameters of complex networks. The complex-network approaches can obtain language classifications as precise as achieved by contemporary word order typology. Clustering experiments point to the fact that the difference between the word form networks and the lemma networks can make for a better classification of languages. In short, the dependency syntactic networks can reflect morphological variation degrees and morphological complexity.

Can chunking reduce syntactic complexity of natural languages

Natural language is a complex adaptive system with multiple levels. The hierarchical structure may have much to do with the complexity of language. Dependency Distance has been invoked to explain various linguistic patterns regarding syntactic complexity. However, little attention has been paid to how the structural properties of language to minimize dependency distance. This article computationally simulates several chunked artificial languages, and shows, through comparison with Mandarin Chinese, that chunking may significantly reduce mean dependency distance of linear sequences. These results suggest that language may have evolved the mechanism of dynamic chunking to reduce the complexity for the sake of efficient communication.

Quantitative typological analysis of Romance languages

Abstract Based on real-text corpora with syntactic annotation, this study quantitatively addressed the following two questions: whether quantitative methods and indexes can point to the diachronic syntactic drifts characterizing the evolution from Latin to Romance languages and whether these methods and indexes can provide evidence to evince the shared syntactic features among Romance languages and define them as a distinctive language subgroup. Our study shows that the distributions of dependency directions are suggestive of positive answers to the above two questions. In addition, the dependency syntactic networks extracted from the dependency treebanks reflect the degree of inflectional variation of a language, and the clustering analysis shows that these parameters, in spite of some imperfections, can also help differentiate Romance languages from Latin diachronically and from other languages synchronically.

Can familiarity lessen the effect of locality? A case study of Mandarin Chinese subjects and the following adverbials

Abstract This paper explores the relation between familiarity of Chinese subjects and the syntactic distance. We propose two hypotheses: (1) contextually given Mandarin Chinese subjects are more likely to be used with long intervening adverbials than contextually new subjects; and (2) subjects with higher word frequency are more likely to be followed by long adverbials than those with lower word frequency. The data from two Mandarin Chinese treebanks provide supportive evidence for the first hypothesis, but not the second. Cognitively, this is probably due to the possibility that contextual givenness, which reflects familiarity, may lessen the effect of locality by increasing the activation level (the accessibility) of the subject and rendering these subjects less susceptible to the memory decay caused by the adverbials intervening between them and the predicate verbs. Subjects are usually the starting point of a sentence, which has a default given–new information structure. Therefore, when organizing a sentence, we are dominantly concerned with the information status (contextual givenness) relative to previous context when choosing the subjects, which may partly accounts for the observed irrelevance between word frequency and the use of adverbials. A sentence is structured based on the information status of the subjects, not their word frequency.

Statistical Analysis of Chinese Phonemic Contrast

Two phonemes that may induce minimal pairs constitute a phonemic contrast. Some phonemic contrasts may disappear for various reasons, which, nevertheless, does not seem to seriously impede linguistic communication. Does it mean that the disappeared phonemic contrasts are unimportant? In our study, we calculated the proportions (here termed degree of contrast) of minimal pairs to the words in which the two contrastive phonemes occur and explored the role of phonemic contrasts in the phonemic combinations. The degree of contrast of phonemes reflects the relation between phonemes. Our results indicate that (1) the average degree of contrast of Chinese phonemes declines exponentially with the increase in the number of syllables, rapidly approaching zero; (2) the average degree of contrast of Chinese consonants that differ from each other in only one distinctive feature and of the consonants that are absent in some Chinese dialects is significantly higher than that of other consonants; (3) the degree of contrast of Chinese consonants that differ from each other in only one distinctive feature is not significantly different from that of the consonants absent in some Chinese dialects; (4) Chinese phonemic combinations exhibit high degree of sparsity, which increases exponentially with the number of syllables and rapidly approaches 1. All these results show that the high degree of sparsity and the low degree of contrast of human languages not only leave enough room for new words, new dialects and new languages to appear but also contribute to effective and reliable communication, because a few phonemic mistakes are not likely to cause wrong decoding (sound recognition) and failed communication.

DDM at Work: Reply to comments on “Dependency distance: A new perspective on syntactic patterns in natural languages”

We provide responses to the commentaries in this volume to evaluate, clarify and extend some of the arguments in Dependency distance: A new perspective on syntactic patterns in natural languages. Evidences show that DDM (dependency distance minimization) is an important linguistic universal, biologically or cognitively motivated, in shaping the language system. As a general tendency, DDM works quite well in theoretical argumentations as well as practical applications. However, this does not mean that DDM is the only linguistic universal that works: it is highly possible that other factors, which might be biologically, physically, socially or culturally motivated, work as well to jointly mold languages.