The paper deals with the processing of Croatian morphology and presents CroDeriV ― a newly developed language resource that contains data about morphological structure and derivational relatedness of verbs in Croatian. In its present shape, CroDeriV contains 14 192 Croatian verbs. Verbs in CroDeriV are analyzed for morphemes and segmented into lexical, derivational and inflectional morphemes. The structure of CroDeriV enables the detection of verbal derivational families in Croatian as well as the distribution and frequency of particular affixes and lexical morphemes. Derivational families consist of a verbal base form and all prefixed or suffixed derivatives detected in available machine readable Croatian dictionaries and corpora. Language data structured in this way was further used for the expansion of other language resources for Croatian, such as Croatian WordNet and the Croatian Morphological Lexicon. Matching the data from CroDeriV on one side and Croatian WordNet and the Croatian Morphological Lexicon on the other resulted in significant enrichment of Croatian WordNet and enlargement of the Croatian Morphological Lexicon.

In recent years large repositories of structured knowledge (DBpedia, Freebase, YAGO) have become a valuable resource for language technologies, especially for the automatic aggregation of knowledge from textual data. One essential component of language technologies, which leverage such knowledge bases, is the linking of words or phrases in specific text documents with elements from the knowledge base (KB). We call this semantic annotation. In the same time, initiatives like Wikidata try to make those knowledge bases less language dependent in order to allow cross-lingual or language independent knowledge access. This poses a new challenge to semantic annotation tools which typically are language dependent and link documents in one language to a structured knowledge base grounded in the same language. Ultimately, the goal is to construct cross-lingual semantic annotation tools that can link words or phrases in one language to a structured knowledge database in any other language or to a language independent representation. To support this line of research we developed what we believe could serve as a gold standard Resource for Evaluating Cross-lingual Semantic Annotation (RECSA). We compiled a hand-annotated parallel corpus of 300 news articles in three languages with cross-lingual semantic groundings to the English Wikipedia and DBPedia. We hope that this new language resource, which is freely available, will help to establish a standard test set and methodology to comparatively evaluate cross-lingual semantic annotation technologies.