Inducing verb classes from frames in Russian: morpho-syntax and semantic roles
The paper presents clustering experiments on Russian verbs based on the statistical data drawn from the Russian FrameBank (framebank.ru). While lexicology has essentially abandoned the idea of syntactic transformations as the primary basis for grouping verbs into semantic classes (Apresjan 1967, Levin 1993), the hypothesis of the same lexical and syntactic distributional profiles underlying lexical clusters is still attractive. In computational linguistics, some attempts have been made to obtain verb classes for English, German and other languages using observable morpho-syntactic and lexical properties of context (Dorr and Jones 1996; Lapata 1999; Schulte im Walde 2006; Lenci 2014, among others). Our experiments on semantic classification of Russian verbs are based on two types of tags embedded in the annotation of argument constructions: a) semantic roles and b) morpho-syntactic patterns. The domain of speech verbs is classified automatically on vectors, and the resulting clusters are contrasted against Babenko (2007)’s semantic classes and three other manual classifications. The classes within the domain of possessive verbs are constructed using rule-based solutions and evaluated against Berkeley FrameNet verb clusters. We conclude that clustering on morpho-syntactic (pure formal) patterns loses the race to more intelligent approaches which take into account semantic roles.