WordNet contains a fair number of synsets with multiple hyperonyms.In parent-child relations, a child can have only one parent (ancestor).Consequently, multiple hyperonymy represents distinct semantic relations.In order to reclassify the multiple hyperonyms, we define a small set of new semantic relations (such as function, origin and form) that cover the various instances of multiple hyperonyms.The synsets with multiple hyperonyms that lead to the same root and belong to the same semantic class were grouped automatically, resulting in semantic patterns that serve as a point of departure for the classification.The proposed changes are based on semantic analysis and may involve the redefinition of one or several multiple hyperonymy relations to new ones, the removal of one or several multiple hyperonymy relations, and rarely the addition of a new hyperonymy relation.As a result, we incorporate the newly defined semantic relations that resolve the former multiple hyperonymy relations and propose an updated WordNet structure without multiple hyperonyms.The resulting WordNet structure without multiple hyperonyms may be used for a variety of purposes that require proper inheritance.

The paper presents the project Semantic Network with a Wide Range of Semantic Relations and its main achievements.The ultimate objective of the project is to expand Princeton WordNet with conceptual frames that define the syntagmatic relations of verb synsets and the semantic classes of nouns felicitous to combine with particular verbs.At this stage of the work: a) over 5,000 WordNet verb synsets have been supplied with manually evaluated FrameNet semantic frames, b) 253 semantic types have been manually mapped to the appropriate WordNet concepts providing detailed ontological representation of the semantic classes of nouns.

The paper discusses the enrichment of WordNet data through merging of Word-Net concepts and Corpus Pattern Analysis (CPA) semantic types.The 253 CPA semantic types are mapped to the respective WordNet concepts.As a result of mapping, the hyponyms of a synset to which a CPA semantic type is mapped inherit not only the respective WordNet semantic primitive but also the CPA semantic type.

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.