Multi-Parser Architecture for Query Processing

摘要

Natural language queries provide a natural means for common people to interact with computers and access to on-line information. Due to the complexity of natural language, the traditional way of using a single grammar for a single language parser leads to an inefficient, fragile, and often very big language processing system. Multi-Parser Architecture (MPA) intends to alleviate these problems, and the modularized MPA also has the advantage of easier portability to new domains and distributed computing. In this paper, we investigate the effect of using different types of parsers on different types of query data in MPA. Three data sets and two types of sub-parsers, particularly a predictive cascading composition for pre-compiled Earley parsers', have been examined. Results show that partitioning grammars leads to superior speed performance for the Earley-style parser across the three data sets. GLR parser is faster than Earley parser in the partitioned case, but it can lead to an excessive memory usage for the un-partitioned case.