Robustness is a key issue for natural language processing in general and parsing in particular,
and many approaches have been explored in the last decade for the design of robust parsing
systems. Among those approaches is shallow or partial parsing, which produces minimal and
incomplete syntactic structures, often in an incremental way. We argue that with a systematic
incremental methodology one can go beyond shallow parsing to deeper language analysis,
while preserving robustness. We describe a generic system based on such a methodology and
designed for building robust analyzers that tackle deeper linguistic phenomena than those
traditionally handled by the now widespread shallow parsers. The rule formalism allows the
recognition of n-ary linguistic relations between words or constituents on the basis of global
or local structural, topological and/or lexical conditions. It offers the advantage of accepting
various types of inputs, ranging from raw to chunked or constituent-marked texts, so for
instance it can be used to process existing annotated corpora, or to perform a deeper analysis
on the output of an existing shallow parser. It has been successfully used to build a deep
functional dependency parser, as well as for the task of co-reference resolution, in a modular
way.