In this paper we present CROMER (CROss-document Main Events and entities Recognition), a novel tool to manually annotate event and entity coreference across clusters of documents. The tool has been developed so as to handle large collections of documents, perform collaborative annotation (several annotators can work on the same clusters), and enable the linking of the annotated data to external knowledge sources. Given the availability of semantic information encoded in Semantic Web resources, this tool is designed to support annotators in linking entities and events to DBPedia and Wikipedia, so as to facilitate the automatic retrieval of additional semantic information. In this way, event modelling and chaining is made easy, while guaranteeing the highest interconnection with external resources. For example, the tool can be easily linked to event models such as the Simple Event Model [Van Hage et al , 2011] and the Grounded Annotation Framework [Fokkens et al. 2013].

This paper presents META-SHARE (www.meta-share.eu), an open language resource infrastructure, and its usage since its Europe-wide deployment in early 2013. META-SHARE is a network of repositories that store language resources (data, tools and processing services) documented with high-quality metadata, aggregated in central inventories allowing for uniform search and access. META-SHARE was developed by META-NET (www.meta-net.eu) and aims to serve as an important component of a language technology marketplace for researchers, developers, professionals and industrial players, catering for the full development cycle of language technology, from research through to innovative products and services. The observed usage in its initial steps, the steadily increasing number of network nodes, resources, users, queries, views and downloads are all encouraging and considered as supportive of the choices made so far. In tandem, take-up activities like direct linking and processing of datasets by language processing services as well as metadata transformation to RDF are expected to open new avenues for data and resources linking and boost the organic growth of the infrastructure while facilitating language technology deployment by much wider research communities and industrial sectors.

We describe META-SHARE which aims at providing an open, distributed, secure, and interoperable infrastructure for the exchange of language resources, including both data and tools. The application has been designed and is developed as part of the T4ME Network of Excellence. We explain the underlying motivation for such a distributed repository for metadata storage and give a detailed overview on the META-SHARE application and its various components. This includes a discussion of the technical architecture of the system as well as a description of the component-based metadata schema format which has been developed in parallel. Development of the META-SHARE infrastructure adopts state-of-the-art technology and follows an open-source approach, allowing the general community to participate in the development process. The META-SHARE software package including full source code has been released to the public in March 2012. We look forward to present an up-to-date version of the META-SHARE software at the conference.

This paper describes the KnowledgeStore, a large-scale infrastructure for the combined storage and interlinking of multimedia resources and ontological knowledge. Information in the KnowledgeStore is organized around entities, such as persons, organizations and locations. The system allows (i) to import background knowledge about entities, in form of annotated RDF triples; (ii) to associate resources to entities by automatically recognizing, coreferring and linking mentions of named entities; and (iii) to derive new entities based on knowledge extracted from mentions. The KnowledgeStore builds on state of art technologies for language processing, including document tagging, named entity extraction and cross-document coreference. Its design provides for a tight integration of linguistic and semantic features, and eases the further processing of information by explicitly representing the contexts where knowledge and mentions are valid or relevant. We describe the system and report about the creation of a large-scale KnowledgeStore instance for storing and integrating multimedia contents and background knowledge relevant to the Italian Trentino region.

We present TextPro, a suite of modular Natural Language Processing (NLP) tools for analysis of Italian and English texts. The suite has been designed so as to integrate and reuse state of the art NLP components developed by researchers at FBK. The current version of the tool suite provides functions ranging from tokenization to chunking and Named Entity Recognition (NER). The system’s architecture is organized as a pipeline of processors wherein each stage accepts data from an initial input or from an output of a previous stage, executes a specific task, and sends the resulting data to the next stage, or to the output of the pipeline. TextPro performed the best on the task of Italian NER and Italian PoS Tagging at EVALITA 2007. When tested on a number of other standard English benchmarks, TextPro confirms that it performs as state of the art system. Distributions for Linux, Solaris and Windows are available, for both research and commercial purposes. A web-service version of the system is under development.

In this paper we present work in progress for the creation of the Italian Content Annotation Bank (I-CAB), a corpus of Italian news annotated with semantic information at different levels. The first level is represented by temporal expressions, the second level is represented by different types of entities (i.e. person, organizations, locations and geo-political entities), and the third level is represented by relations between entities (e.g. the affiliation relation connecting a person to an organization). So far I-CAB has been manually annotated with temporal expressions, person entities and organization entities. As we intend I-CAB to become a benchmark for various automatic Information Extraction tasks, we followed a policy of reusing already available markup languages. In particular, we adopted the annotation schemes developed for the ACE Entity Detection and Time Expressions Recognition and Normalization tasks. As the ACE guidelines have originally been developed for English, part of the effort consisted in adapting them to the specific morpho-syntactic features of Italian. Finally, we have extended them to include a wider range of entities, such as conjunctions.