Robust Processing of Natural Language

Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by ``normal'' means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even noticing them. The paper analyses the necessary preconditions for achieving a higher degree of robustness in natural language processing and suggests a quite different approach based on a procedure for structural disambiguation. It not only offers the possibility to cope with robustness issues in a more natural way but eventually might be suited to accommodate quite different aspects of robust behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro, pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture Notes in Computer Science, Springer 199

Knowledge-based intelligent error feedback in a Spanish ICALL system

This paper describes the Spanish ICALL system ESPADA which helps language learners to improve their syntactical knowledge. The most important parts of ESPADA for the learner are a Demonstration Module and an Analysis Module. The Demonstration Module provides animated presentation of selected grammatical information. The Analysis Module is able to parse ill-formed sentences and to give adequate feedback on 28 different error types from different levels of language use (syntax, semantics, agreement). It contains a robust chart-based island parser which uses a combination of mal-rules and constraint relaxation to ensure that learner input can be analysed and appropriate error feedback can be generated