The paper presents the work on the selection, semantic annotation and classification of a group of verbs from WordNet, characterized with the semantic primitive ‘verbs of contact’ that belong to the common Bulgarian lexis. The selection of the verb set using both different criteria: statistical information from corpora, WordNet Base concepts and AoA as a criterion, is described. The focus of the work is on the process of the verbs’ of contact semantic annotation using the combined information from two language resources - WordNet and FrameNet. The verbs of contact from WordNet are assigmed semantic frames from FrameNet and then grouped in semantic subclasses using both their place in the WordNet hierarchy, the semantic restrictions on their frame elements and the corresponding syntactic realization. At the end we offer some conclusions on the classification of ‘verbs of contact’ in semantic subtypes.
This paper represents a description of Bulgarian verbal computer terms with a view to the specifics of their translation in English. The study employs a subset of 100 verbs extracted from the Bulgarian WordNet (BulNet) and from the internet. The analysis of their syntactic and semantic structure is a part of a study of the general lexis of Bulgarian. The aim of the paper is to (1) identify some problem areas of the description and translation of general lexis verbs, (2) offer an approach to the semantic description of metaphor-based terms from the perspective of Frame Semantics; (3) raise questions about the definition of general lexis with respect to Bulgarian and across languages.
In this paper we focus on verbal multiword expressions (VMWEs) in Bulgarian and Romanian as reflected in the wordnets of the two languages. The annotation of VMWEs relies on the classification defined within the PARSEME Cost Action. After outlining the properties of various types of VMWEs, a cross-language comparison is drawn, aimed to highlight the similarities and the differences between Bulgarian and Romanian with respect to the lexicalization and distribution of VMWEs. The contribution of this work is in outlining essential features of the description and classification of VMWEs and the cross-language comparison at the lexical level, which is essential for the understanding of the need for uniform annotation guidelines and a viable procedure for validation of the annotation.
This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.