Adapting VerbNet to French using existing resources

Classifying French Verbs Using French and English Lexical Resources

We present a novel approach to the automatic acquisition of a Verbnet like classification of French verbs which involves the use (i) of a neural clustering method which associates clusters with features, (ii) of several supervised and unsupervised evaluation metrics and (iii) of various existing syntactic and semantic lexical resources. We evaluate our approach on an established test set and sh...

Making Use of Existing Lexical Resources to Build a Verbnet like Classification of French Verbs. (Acquisition de classes verbales pour le français)

Investigating the cross-linguistic potential of VerbNet-style classification

Verb classes which integrate a wide range of linguistic properties (Levin, 1993) have proved useful for natural language processing (NLP) applications. However, the real-world use of these classes has been limited because for most languages, no resources similar to VerbNet (KipperSchuler, 2005) are available. We apply a verb clustering approach developed for English to French – a language for w...

Improvement of VerbNet-like resources by frame typing

Verb@net is a French lexicon developed by “translation” of its English counterpart — VerbNet (Kipper-Schuler, 2005) — and treatment of the specificities of French syntax (Pradet et al., 2014; Danlos et al., 2016). One difficulty encountered in its development springs from the fact that the list of (potentially numerous) frames has no internal organization. This paper proposes a type system for ...

Construction of Conceptual Graph Representation of Texts

This paper describes a system for constructing conceptual graph representation of text by using a combination of existing linguistic resources (VerbNet and WordNet). We use a twostep approach, by firstly identifying the semantic roles in a sentence, and then using these roles, together with semi-automatically compiled domain-specific knowledge to construct the conceptual graph representation.