Feature unification in parsing has previously used either inefficient Prolog programs, or LISP programs implementing early pre-WAM Prolog models of unification involving searches of binding lists, and the copying of rules to generate edges: features within rules and edges have traditionally been expressed as lists or functions, with clarity being preferred to speed of processing. As a result, parsing takes about 0·5 seconds for a 7-word sentence. Our earlier work produced an optimised chart parser for a non-unification context-free-grammar that achieved 5 ms parses, with high-ambiguity sentences involving hundreds of edges, using the grammar and sentences from Tomita's work on shift-reduce parsing with multiple stack branches. A parallel logic card design resulted that would speed this by a further factor of at least 17. The current paper extends this parser to treat a much more complex unification grammar with structures, using extensive indexing of rules and edges and the optimisations of top-down filtering and look-ahead, to demonstrate where unification occurs during parsing. Unification in parsing is distinguished from that in Prolog, and four alternative schemes for storing features and performing unification are considered, including the traditional binding-list method and three other methods optimised for speed for which overall unification times are calculated. Parallelisation of unification using cheap logic hardware is considered, and estimates show that unification will negligibly increase the parse time of our parallel parser card. Preliminary results are reported from a prototype serial parser that uses the fourth most efficient unification method, and achieves 7 ms for 7-word sentences, and under 1 s for a 36-word 360-way ambiguous sentence with 10,000 edges, on a conventional workstation.

Every word in the lexicon of a natural language is used distinctly from all the other words. A word expert is a small expert system-like module for processing a particular word based on other words in its vicinity. A word expert exploits the idiosyncratic nature of a word by using a set of context testing decision rules that test the identity and placement of context words to infer the word's role in the passage.

The main application of word experts is disambiguating words. Work on word experts has never fully recognized previous related work, and a comprehensive review of that work would therefore contribute to the field. This paper both provides such a review, and describes guidelines and considerations useful in the design and construction of word expert based systems.

The Portable Extendable Traffic Information Collator (POETIC) is an information extraction system that extracts traffic information from free text occurring in police incident logs and initiates (simulated) broadcasts of traffic bulletins to motorists when appropriate. POETIC is a second stage prototype system; the initial prototype (TIC, see Evans and Hartley 1990) was limited to the practices and requirements of a single police force. In POETIC, the architecture and data representations have been generalised to make the system tailorable to many different police force ‘domains’. In this paper we describe these developments, and report on tests of the system on authentic input data from three police domains.