This paper describes an ongoing effort towards expanding the semantic and conceptual description of verbs in WordNet by combining information from two other resources, FrameNet and VerbNet, as well as enriching the verbs' description with syntactic patterns extracted from the three resources.The conceptual description of verb synsets is provided by assigning a FrameNet frame which provides the relevant set of frame elements denoting the predicate's participants and props.This information is supplemented by assigning a VerbNet class and the set of semantic roles associated with it.The information extracted from FrameNet and Verb-Net and assigned to a synset is aligned (semiautomatically with subsequent manual corrections) at the following levels: (i) FrameNet frame: VerbNet class; (ii) FrameNet frame elements: VerbNet semantic roles; (iii) FrameNet semantic types and restrictions: VerbNet selectional restrictions.We then link the syntactic patterns associated with the units in FrameNet, VerbNet and WordNet, by unifying their representation and by matching the corresponding patterns at the level of syntactic groups.The alignment of the semantic components and their syntactic realisations is essential for the better exploitation of the abundance of information across resources, including shedding light on cross-resource similarities, discrepancies and inconsistencies.The syntactic patterns can facilitate the extraction of examples illustrating the use of verb synset literals in corpora and their semantic characterisation through the association of the syntactic groups with the components of semantic description (frame elements or semantic roles) and can be employed in various tasks requiring semantic and syntactic description.The resource is publicly available to the community.The components of the conceptual description are visualised showing the links to the original resources each component is drawn from.

We present here the results of a morphosemantic analysis of the verb-noun pairs in the Princeton WordNet as reflected in the standoff file containing pairs annotated with a set of 14 semantic relations.We have automatically distinguished between zero-derivation and affixal derivation in the data and identified the affixes and manually checked the results.The data show that for each semantic relation an affix prevails in creating new words, although we cannot talk about their specificity with respect to such a relation.Moreover, certain pairs of verb-noun semantic primes are better represented for each semantic relation, and some semantic clusters (in the form of WordNet subtrees) take shape as a result.We thus employ a large-scale data-driven linguistically motivated analysis afforded by the rich derivational and morphosemantic description in WordNet to the end of capturing finer regularities in the process of derivation as represented in the semantic properties of the words involved and as reflected in the structure of the lexicon.

The paper presents current efforts towards linking two large lexical semantic resources -WordNet and FrameNet -to the end of their mutual enrichment and the facilitation of the access, extraction and analysis of various types of semantic and syntactic information.In the second part of the paper, we go on to examine the relation of inheritance and other semantic relations as represented in WordNet and FrameNet and how they correspond to each other when the resources are aligned.We discuss the implications with respect to the enhancement of the two resources through the definition of new relations and the detailisation of conceptual frames.1

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.