Multi-parser architecture for query processing

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 large 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 have been examined. Results show that partitioning grammars leads to superior speed performance for the Earley parser across the three data sets. The GLR parser is faster than the Earley parser for the partitioned grammar, but the GLR parser may have excessive memory usage for the un-partitioned grammars.