In this paper we present a web interface to study Italian through the access to read Italian literature. The system allows to browse the content, search for specific words and listen to the correct pronunciation produced by native speakers in a given context. This work aims at providing people who are interested in learning Italian with a new way of exploring the Italian culture and literature through a web interface with a search module. By submitting a query, users may browse and listen to the results through several modalities including: a) the voice of a native speaker: if an indexed audio track is available, the user can listen either to the query terms or to the whole context in which they appear (sentence, paragraph, verse); b) a synthetic voice: the user can listen to the results read by a text-to-speech system; c) an avatar: the user can listen to and look at a talking head reading the paragraph and visually reproducing real speech articulatory movements. In its up to date version, different speech technologies currently being developed at ISTC-CNR are implemented into a single framework. The system will be described in detail and hints for future work are discussed.

A novel method to automatically associate ontological concepts to their realisations in texts is presented. The method has been developed in the context of the Papyrus project to annotate texts and audio transcripts with a set of relevant concepts from the Papyrus News Ontology. To avoid strong dependency on a specific ontology, the annotation process starts by performing a Wikipedia-based annotation of news items: the most relevant keywords are detected and the Wikipedia pages that best describe their actual meaning are identified. In a later step this annotation is translated into an Ontology-based one: keywords are connected to the most appropriate ontology classes on the basis of a relatedness measure that relies on Wikipedia knowledge. Wikipedia-annotation provides a domain independent abstraction layer that simplify the adaptation of the approach to other domains and ontologies. Evaluation has been performed on a set of manually annotated news, resulting in 58% F1 score for relevant Wikipedia pages and 64% for relevant ontology concepts identification.