Traditionally, rich, constraint-based grammatical resources have been hand-coded. Scaling such resources beyond toy fragments to unrestricted, real text is knowledge-intensive, time-consuming and expensive.

The work reported in my thesis is part of a larger project to automate as much as possible the construction of wide-coverage, deep, constraint-based grammatical resources from treebanks. The Penn-II treebank is a large collection of parse-annotated newspaper text. We have designed a Lexical-Functional Grammar (LFG) f-structure annotation algorithm to automatically annotate this treebank with f-structure information approximating to basic predicate-argument or dependency structures. We then use the f-structure-annotated treebank resource to automatically extract grammars and lexical resources for parsing new text into f-structures.

We have designed and implemented the Treebank Tool Suite (TTS) to support the linguistic work that seeds the automatic f-structure annotation algorithm and the F-Structure Annotation Tool (FSAT) to validate and visualise the results of automatic f-structure annotation.

We have designed and implemented two PCFG-based probabilistic parsing architectures for parsing unseen text into f-structures: the pipeline and the integrated model. Both architectures parse raw text into basic, but possibly incomplete, predicate-argument structures ("proto f-structures") with long distance dependencies (LDDs) unresolved.

We have designed and implemented a method for automatically resolving LDDs at f-structure level based on a finite approximation of functional uncertainty equations automatically acquired from the f-structure-annotated treebank resource.

We have also ported our grammar induction methodology to German and the TIGER treebank resource.

We have developed a method for treebank-based, wide-coverage, deep, constraint-based grammar acquisition. The resulting PCFG-based LFG approximations parse the Penn-II treebank with wider coverage (measured in terms of complete spanning parse) and parsing results comparable to or better than those achieved by the best hand-crafted grammars, with, we believe, considerably less grammar development effort. We believe that our approach successfully addresses the knowledge-acquisition bottleneck (familiar from rule-based approaches to AI and NLP) in wide-coverage, constraint-based grammar development. Our approach can provide an attractive, wide-coverage, multilingual, deep, constraint-based grammar acquisition paradigm.