Commission of the EC, Telematics Applications Programme, Language Engineering, project LE1-2111
Participants
- Consorzio Pisa Ricerche, Italy (Coordinator)
- Cambridge University Computer Laboratory, UK &s; School of Cognitive and Computing Sciences, University of Sussex, UK
- Daimler-Benz, Germany
- Sharp Laboratories of Europe, UK
- IMS, University of Stuttgart, Germany
- Rank Xerox Research Centre, France
The University of Sussex is subcontracting from Cambridge University. The project team at the University of Sussex is:
Goals of SPARKLE
SPARKLE aims to develop 'shallow' parsing technology in 4 European languages together with corpus-based lexical acquisition techniques, and deploy parsers in multilingual information retrieval and speech dialogue systems.
The first goal of SPARKLE is to produce generic software able to reliably produce a unique, correct but simple phrasal-level syntactic analysis of naturally-occurring free text. This software will be capable of practical use for processing of substantial quantities of such (corpus) material. Such phrasal-parsers will be generic in the sense that they aim to be compatible with a variety of extant approaches to lemmatisation, morphological analysis and lexical syntactic tagging and aim to be straightforwardly parameterisable for different (European) languages.
The second goal is to develop a lexical acquisition system capable of learning subcategorisation, argument structure and semantic selection preferences for individual predicates from free text containing instances of such predicates. The lexicon acquisition system will also be developed as a parameterisable multilingual software tool incorporating language-independent and-dependent linguistic knowledge concerning membership of predicates in broad semantic classes, (diathesis) alternations, the linking of arguments to thematic relations.
Work on SPARKLE at Sussex
We are working on:
- syntactic annotation schemes for corpora and evaluation standards for parsers
- developing a robust and accurate phrasal parser of English
- a system for automatically acquiring subcategorisation information from corpora
- techniques for modeling semantic type
- acquisition of selectional preferences
- the automatic recognition of diathesis alternations