Customizable Modular Lexicalized Parsing

Dierent NLP applications have dierent eciency constraints (i.e. quality of the results and throughput) that reect on each core linguistic component. Syntactic processors are basic modules in some NLP application. A customization that permits the performance control of these components enables their reuse in dierent application scenarios. Throughput has been commonly improved using partial syntactic processors. On the other hand, specialized lexicons are generally employed to improve the quality of the syntactic material produced by speci c parsing (sub)process (e.g. verb argument detection or PPattachment disambiguation). Building upon the idea of grammar strati cation, in this paper a method to push modularity and lexical sensitivity, in parsing, in view of customizable syntactic analysers is presented. A framework for modular parser design is proposed and its main properties are discussed

AI/NLP TECHNOLOGIES APPLIED TO SPACECRAFT MISSION DESIGN

In this paper we propose the model of a prototypical NLP architecture of an information access system to support a team of experts in a scientific design task, in a shared and heterogeneous framework. Specifically, we believe AI/NLP can be helpful in several tasks, such as the extraction of implicit information needs enclosed in meeting minutes or other documents, analysis of explicit information needs expressed through Natural Language, processing and indexing of document collections, extraction of required information from documents, modeling of a common knowledge base, and, finally, identification of important concepts through the automatic extraction of terms. In particular, we envisioned this architecture in the specific and practical scenario of the Concurrent Design Facility (CDF) of the European Space Agency (ESA), in the framework of the SHUMI project (Support To HUman Machine Interaction) developed in collaboration with the ESA/ESTEC - ACT (Advanced Concept Team)

Parallels between machine and brain decoding

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies