With growing interest in the creation and search of linguistic annotations that form general graphs (in contrast to formally simpler, rooted trees), there also is an increased need for infrastructures that support the exploration of such representations, for example logical-form meaning representations or semantic dependency graphs. In this work, we heavily lean on semantic technologies and in particular the data model of the Resource Description Framework (RDF) to represent, store, and efficiently query very large collections of text annotated with graph-structured representations of sentence meaning.

In order to handle the increasing amount of textual information today available on the web and exploit the knowledge latent in this mass of unstructured data, a wide variety of linguistic knowledge and resources (Language Identification, Morphological Analysis, Entity Extraction, etc.). is crucial. In the last decade LRaas (Language Resource as a Service) emerged as a novel paradigm for publishing and sharing these heterogeneous software resources over the Web. In this paper we present an overview of Linguagrid, a recent initiative that implements an open network of linguistic and semantic Web Services for the Italian language, as well as a new approach for enabling customizable corpus-based linguistic services on Linguagrid LRaaS infrastructure. A corpus ingestion service in fact allows users to upload corpora of documents and to generate classification/clustering models tailored to their needs by means of standard machine learning techniques applied to the textual contents and metadata from the corpora. The models so generated can then be accessed through proper Web Services and exploited to process and classify new textual contents.

This paper focuses on the central role played by lexical information in the task of Recognizing Textual Entailment. In particular, the usefulness of lexical knowledge extracted from several widely used static resources, represented in the form of entailment rules, is compared with a method to extract lexical information from Wikipedia as a dynamic knowledge resource. The proposed acquisition method aims at maximizing two key features of the resulting entailment rules: coverage (i.e. the proportion of rules successfully applied over a dataset of TE pairs), and context sensitivity (i.e. the proportion of rules applied in appropriate contexts). Evaluation results show that Wikipedia can be effectively used as a source of lexical entailment rules, featuring both higher coverage and context sensitivity with respect to other resources.

This paper presents the QALL-ME benchmark, a multilingual resource of annotated spoken requests in the tourism domain, freely available for research purposes. The languages currently involved in the project are Italian, English, Spanish and German. It introduces a semantic annotation scheme for spoken information access requests, specifically derived from Question Answering (QA) research. In addition to pragmatic and semantic annotations, we propose three QA-based annotation levels: the Expected Answer Type, the Expected Answer Quantifier and the Question Topical Target of a request, to fully capture the content of a request and extract the sought-after information. The QALL-ME benchmark is developed under the EU-FP6 QALL-ME project which aims at the realization of a shared and distributed infrastructure for Question Answering (QA) systems on mobile devices (e.g. mobile phones). Questions are formulated by the users in free natural language input, and the system returns the actual sequence of words which constitutes the answer from a collection of information sources (e.g. documents, databases). Within this framework, the benchmark has the twofold purpose of training machine learning based applications for QA, and testing their actual performance with a rapid turnaround in controlled laboratory setting.

Entailment rules are rules where the left hand side (LHS) specifies some knowledge which entails the knowledge expressed n the RHS of the rule, with some degree of confidence. Simple entailment rules can be combined in complex entailment chains, which n turn are at the basis of entailment-based reasoning, which has been recently proposed as a pervasive and application independent approach to Natural Language Understanding. We present the first elease of a large-scale repository of entailment rules at the lexical level, which have been derived from a number of available resources, including WordNet and a word similarity database. Experiments on the PASCAL-RTE dataset show that this resource plays a crucial role in recognizing textual entailment.