How tight is your language? A semantic typology based on Mutual Information

Levshina, N. (2020). How tight is your language? A semantic typology based on Mutual Information. In K. Evang, L. Kallmeyer, R. Ehren, S. Petitjean, E. Seyffarth, & D. Seddah (Eds.), Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories (pp. 70-78). Düsseldorf, Germany: Association for Computational Linguistics. doi:10.18653/v1/2020.tlt-1.7.
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.
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