High-Performance Multi-Pass Unication Parsing

Parsing natural language is an attempt to discover some structure in a text (or textual representation) generated by a person. This structure can be put to a variety of uses, including machine translation, grammar conformance checking, and determination of prosody in text-to-speech tasks. Recent theories of Syntax use Unication to better describe the intricacies of natural language [137]. For parsing systems, unication techniques have been either added to a context-free base system [152, 40, 4, 23], or replaced the context-free base entirely [118, 135, 45] (possibly putting it back later [136]). The seemingly small step of adding unication has opened a Pandora’s Box of computational complexity, increasing the difculty of the problem from polynomial [48] to somewhere between NP-complete and intractable, depending on the details of the unication system and how it was added [10]. Worse, unication on a context-free base parser can break the packing technique used to address the problem of ambiguity, leading to exponential blow-ups of the parser’s performance in both space and time in practice. I propose the use of a multi-pass strategy to avoid these problems in practice. I describe a parser which combines the use of shallow, simple value unication with some approximation techniques in order to nd a covering packed parse-forest. This parseforest is then searched for a single-best fully-unifying value; the scoring system which drives the heuristic search encodes linguistically-based disambiguation preferences. The resulting two-pass parser is compared to an ordinary single-pass parser in the context of a heavy-weight knowledge-based machine translation system. The two-pass parser is shown to be competitive with the single-pass parser on average data, both in terms of time and space. It is also shown to be able to avoid a common class of ambiguity blow-up that the single-pass parser is subject to. These results indicate that the multi-pass technique, interleaving some of the unication equations in the parse, is the superior approach for heavy-weight unication parsing.