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

Languages differ in the degree of semantic flexibility of their syntactic roles. For example, English 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 languages 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 typologically 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 (intransitive 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 languages. We also demonstrate that verb-final languages tend to have a tighter relationship between lexemes and syntactic roles, which helps language users to recognize thematic roles early during comprehension. 1 Theoretical background and aims of the paper This paper proposes a quantitative bottom-up corpus-based approach to cross-linguistic comparison, determining how tightly or loosely different lexemes can be mapped on basic syntactic roles. The idea goes back to Hawkins (1986: 121– 127, 1995; see also Müller-Gotama 1994), who coined the terms ‘tight-fit’ and ‘loose-fit’ languages. The former have unique surface forms that map onto more constrained meanings, whereas the latter have more vague forms with less constrained meanings. For instance, Present-Day English has fewer semantic restrictions on the subject and object than Old English, German or Russian. Consider several examples below. (1) a. Locative: This tent sleeps four. b. Temporal: 2020 witnessed a spread of the highly infectious coronavirus disease. c. Instrument: 10 Euros will buy you a meal. d. Source: The roof leaks water. While these sentences are perfectly acceptable in English, their German or Russian equivalents would be unacceptable or strange. This means that subjects in English are less semantically restricted than subjects in German and Russian (see also Plank 1984). Tightness and looseness have several components. Semantic flexibility of arguments is only one of them. Other features of tight languages include formal case marking, avoidance of raisings and long WH-movements and lower reliance on context in interpretation. Languages can change their degree of tightness. English is a well-known example of shifting from tight to loose (Hawkins 1986). As the case was lost, the zero-marked NPs in Middle English became more dependent on the verb for theta-role assignment. This is why the rigid SVO order emerged, which helps language users to understand correctly who did what to whom. Also, new instrumental and locative subjects as in (1) became possible, which used to be the case only in prepositional phrases. In the previous work, the judgements about tightness and looseness were made introspectively and qualitatively. This paper presents a method that allows one to quantify these differences objectively with the help of corpus data. We only focus on the fit between syntactic roles and semantics of lexemes in this study. As a proxy for semantics, we extract frequencies of lexemes in different syntactic roles from syntactically parsed corpora (see Section 2). Next, we compute how much these frequencies diverge from the total frequencies of the roles with the help of the Mutual Information metric. The higher a score, the tighter the language (see Section 3). The scores are then compared with the existing classification of languages. We find a close correspondence between the scores (see Section 4). The scores are computed for lemmas alone and for lemmas plus multiword units. We also investigate the correlation between tightness and the proportion of verb-final frames in a corpus (Section 5). Section 6 provides the conclusions and an outlook.