Publications

Abstract

Languages differ in the degree of semantic flexibility of their syntactic roles. For example, Eng-
lish and Indonesian are considered more flexible with regard to the semantics of subjects,
whereas German and Japanese are less flexible. In Hawkins’ classification, more flexible lan-
guages are said to have a loose fit, and less flexible ones are those that have a tight fit. This
classification has been based on manual inspection of example sentences. The present paper
proposes a new, quantitative approach to deriving the measures of looseness and tightness from
corpora. We use corpora of online news from the Leipzig Corpora Collection in thirty typolog-
ically and genealogically diverse languages and parse them syntactically with the help of the
Universal Dependencies annotation software. Next, we compute Mutual Information scores for
each language using the matrices of lexical lemmas and four syntactic dependencies (intransi-
tive subjects, transitive subject, objects and obliques). The new approach allows us not only to
reproduce the results of previous investigations, but also to extend the typology to new lan-
guages. We also demonstrate that verb-final languages tend to have a tighter relationship be-
tween lexemes and syntactic roles, which helps language users to recognize thematic roles early
during comprehension.

Abstract

The notion of efficient trade-offs is frequently used in functional linguis-tics in order to explain language use and structure. In this paper I argue that this notion is more confusing than enlightening. Not every negative correlation between parameters represents a real trade-off. Moreover, trade-offs are usually reported between pairs of variables, without taking into account the role of other factors. These and other theoretical issues are illustrated in a case study of linguistic cues used in expressing “who did what to whom”: case marking, rigid word order and medial verb posi-tion. The data are taken from the Universal Dependencies corpora in 30 languages and annotated corpora of online news from the Leipzig Corpora collection. We find that not all cues are correlated negatively, which ques-tions the assumption of language as a zero-sum game. Moreover, the cor-relations between pairs of variables change when we incorporate the third variable. Finally, the relationships between the variables are not always bi-directional. The study also presents a causal model, which can serve as a more appropriate alternative to trade-offs.

Abstract

The present paper discusses the benefits and challenges of token-based typology, which takes into account the frequencies of words and constructions in language use. This approach makes it possible to introduce new criteria for language classification, which would be difficult or impossible to achieve with the traditional, type-based approach. This point is illustrated by several quantitative studies of word order variation, which can be measured as entropy at different levels of granularity. I argue that this variation can be explained by general functional mechanisms and pressures, which manifest themselves in language use, such as optimization of processing (including avoidance of ambiguity) and grammaticalization of predictable units occurring in chunks. The case studies are based on multilingual corpora, which have been parsed using the Universal Dependencies annotation scheme.