Verb valence information can be derived from corpora by using subcorpora of typical sentences that are constructed in a language independent manner based on frequent POS structures. The inspection of typical sentences with a fixed verb in a certain position can show the valence information directly. Using verb fingerprints, consisting of the most typical sentence patterns the verb appears in, we are able to identify standard valence patterns and compare them against a language’s valence profile. With a very limited number of training data per language, valence information for other verbs can be derived as well. Based on the Norwegian valence patterns we are able to find comparative patterns in German where typical sentences are able to express the same situation in an equivalent way and can so construct verb valence pairs for a bilingual PolyVal dictionary. This contribution discusses this application with a focus on the Norwegian valence dictionary NorVal.

Traditionally, a lexicographer identifies the lexical items to be added to a dictionary. Here we present a corpus-based approach to dictionary compilation and describe a procedure that derives a Twi dictionary from a TypeCraft corpus of Interlinear Glossed Texts. We first extracted a list of unique words. We excluded words belonging to different dialects of Akan (mostly Fante and Abron). We corrected misspellings and distinguished English loan words to be integrated in our dictionary from instances of code switching. Next to the dictionary itself, one other resource arising from our work is a lexicographical model for Akan which represents the lexical resource itself, and the extended morphological and word class inventories that provide information to be aggregated. We also represent external resources such as the corpus that serves as the source and word level audio files. The Twi dictionary consists at present of 1367 words; it will be available online and from an open mobile app.

MultiVal is a valence lexicon derived from lexicons of computational HPSG grammars for Norwegian, Spanish and Ga (ISO 639-3, gaa), with altogether about 22,000 verb entries and on average more than 200 valence types defined for each language. These lexical resources are mapped onto a common set of discriminants with a common array of values, and stored in a relational database linked to a web demo and a wiki presentation. Search discriminants are ‘syntactic argument structure’ (SAS), functional specification, situation type and aspect, for any subset of languages, as well as the verb type systems of the grammars. Search results are lexical entries satisfying the discriminants entered, exposing the specifications from the respective provenance grammars. The Ga grammar lexicon has in turn been converted from a Ga Toolbox lexicon. Aside from the creation of such a multilingual valence resource through converging or converting existing resources, the paper also addresses a tool for the creation of such a resource as part of corpus annotation for less resourced languages.

The paper presents advances in the use of semantic features and interlingua relations for word sense disambiguation (WSD) as part of unification-based deep processing grammars. Formally we present an extension of Minimal Recursion Semantics, introducing sortal specifications as well as linterlingua semantic relations as a means of semantic decomposition.