A system for the automatic production of controlled index terms is presented using linguistically-motivated techniques. This includes a finite-state part of speech tagger, a derivational morphological processor for analysis and generation, and a unificationbased shallow-level parser using transformational rules over syntactic patterns. The contribution of this research is the success- ful combination of parsing over a seed term list coupled with derivational morphology to achieve greater coverage of multi-word terms for indexing and retrieval. Final results are evaluated for precision and recall, and implications for indexing and retrieval are discussed.

Windowing techniques play a key role in information retrieval. Previous works have suggested that the quality of access to information relies heavily on the characteristics of the windows. This study provides a linguistic approach to text windowing through an extraction of term variants with the help of a partial parser. The syntactic grounding of the method ensures that words observed within restricted spans are lexically related and that spurious word co-occurrences are ruled out with a good level of confidence. The system is computationally tractable on large corpora and large lists of terms. Illustrative examples of term variations from a large medical corpus are given. An experimental evaluation of the method shows that only a small proportion of co-occurring words are lexically related and motivates the call for natural language parsing techniques in text windowing. 1. INTRODUCTION The notion of text window -- a span of contiguous words within a document -- is crucial for severa...

An automatic processor of written French language is described. This processor uses syntactic and semantic informations about words in order to construct a semantic net representing the meaning of the sentences. The structure of the network and the principles of the parser are explained. An application to the processing of the medical records is then discussed.