This paper presents an unsupervised and domain independent semantic analysis that outputs two types of formal representations: discourse representations structures and flat scope-free logical forms. This semantic analysis is built on top of dependency relations produced by a statistical syntactic parser, and is generated by a grammar of patterns named α-grammar. The interest of this grammar lies in building a clear linguistically-grounded syntax-semantic interface using a representation (dependencies) commonly used in the natural language processing community. The paper also explains how semantic representations can be annotated using an upper-level ontology, thus enabling further inference capabilities. The evaluation of the α-Grammar on a hand-made gold standard and on texts from the STEP 2008 Shared Task competition shows the interest of the approach. © 2011 Springer-Verlag.

This paper describes a method to perform automated semantic annotation of named entities contained in large corpora. The semantic annotation is made in the context of the Semantic Web. The method is based on an algorithm that compares the set of words that appear before and after the name entity with the content of Wikipedia articles, and identifies the more relevant one by means of a similarity measure. It then uses the link that exists between the selected Wikipedia entry and the corresponding RDF description in the Linked Data project to establish a connection between the named entity and some URI in the Semantic Web. We present our system, discuss its architecture, and describe an algorithm dedicated to ontological disambiguation of named entities contained in large-scale corpora. We evaluate the algorithm, and present our results. © 2011 Springer-Verlag.

This paper presents a high level view of a project which aims at improving the communication between people who do not share the same language by using a 3D animation upper layer to lift the inherent limitations of written text. We will produce animation using the Collada file format, a standard based on the XML format. This project (GITAN ) has started in January 2010, and we expect to have the first results by the end of 2010. A limited set of sentences will validate the global process and help us refine the tools developed in the pipeline. © Springer-Verlag 2010.

We describe our contribution to the Generation Challenge 2010 for the tasks of Named Entity Recognition and coreference detection (GREC-NER). To extract the NE and the referring expressions, we employ a combination of a Part of Speech Tagger and the Conditional Random Fields (CRF) learning technique. We finally experiment an original algorithm to detect co-references. We conclude with discussion about our system performances.