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Looking up for an unknown word is the most frequent use of a dictionary. For languages both agglutinative and inflectional, such as Georgian, this can be quite challenging because an inflected form can be very far from the lemmas used by the target dictionary. In addition, there is no consensus among Georgian lexicographers on which lemmas represent a verb in dictionaries. It further complicates dictionaries access. Kartu-Verbs is a base of inflected forms of Georgian verbs accessible by a logical information system. It currently contains more than 5 million inflected forms related to more than 16,000 verbs for 11 tenses; each form can have 11 properties; there are more than 80 million links in the base. This demonstration shows how, from any inflected form, we can find the relevant lemma to access any dictionary. Kartu-Verbs can thus be used as a front-end to any Georgian dictionary.
In this paper, we present LexMeta, a metadata model for the description of human-readable and computational lexical resources in catalogues. Our initial motivation is the extension of the LexBib knowledge graph with the addition of metadata for dictionaries, making it a catalogue of and about lexicographical works. The scope of the proposed model, however, is broader, aiming at the exchange of metadata with catalogues of Language Resources and Technologies and addressing a wider community of researchers besides lexicographers. For the definition of the LexMeta core classes and properties, we deploy widely used RDF vocabularies, mainly Meta-Share, a metadata model for Language Resources and Technologies, and FRBR, a model for bibliographic records.
In this paper we investigate the coverage of the two knowledge sources WordNet and Wikipedia for the task of bridging resolution. We report on an annotation experiment which yielded pairs of bridging anaphors and their antecedents in spoken multi-party dialog. Manual inspection of the two knowledge sources showed that, with some interesting exceptions, Wikipedia is superior to WordNet when it comes to the coverage of information necessary to resolve the bridging anaphors in our data set. We further describe a simple procedure for the automatic extraction of the required knowledge from Wikipedia by means of an API, and discuss some of the implications of the procedure’s performance.