Hideto Tomabechi

Quasi-Destructive Graph Unification

Graph unification is the most expensive part of unification-based grammar parsing. It often takes over 90% of the total parsing time of a sentence. We focus on two speed-up elements in the design of unification algorithms: 1) elimination of excessive copying by only copying successful unifications, 2) Finding unification failures as soon as possible. We have developed a scheme to attain these two elements without expensive overhead through temporarily modifying graphs during unification to eliminate copying during unification. We found that parsing relatively long sentences (requiring about 500 top-level unifications during a parse) using our algorithm is approximately twice as fast as parsing the same sentences using Wroblewskis algorithm.

Quasi-destructive graph unification with structure-sharing

Graph unification remains the most expensive part of unification-based grammar parsing. We focus on one speed-up element in the design of unification algorithms: avoidance of copying of unmodified subgraphs. We propose a method of attaining such a design through a method of structure-sharing which avoids log(d) overheads often associated with structure-sharing of graphs without any use of costly dependency pointers. The proposed scheme eliminates redundant copying while maintaining the quasi-destructive schemes ability to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions.

Ambiguity resolution in the dmTrans Plus

We present a cost-based (or energy-based) model of disambiguation. When a sentence is ambiguous, a parse with the least cost is chosen from among multiple hypotheses. Each hypothesis is assigned a cost which is added when: (1) a new instance is created to satisfy reference success, (2) links between instances are created or removed to satisfy constraints on concept sequences, and (3) a concept node with insufficient priming is used for further processing. This method of ambiguity resolution is implemented in DMTRANS PLUS, which is a second generation bi-directional English/Japanese machine translation system based on a massively parallel spreading activation paradigm developed at the Center for Machine Translation at Carnegie Mellon University.