Sentence parsing with weak grammatical constraints

This paper compares the recognition accuracy obtained in forming sentence hypotheses using several parsers based on different types of weak statistical models of syntax and semantics. The inputs to the parsers were word hypotheses generated from simulated acoustic-phonetic labels. Grammatical constraints are expressed by trigram models of sequences of lexical or semantic labels, or by a finite-state network of the semantic labels. When the input to the parser is of high quality, the more restrictive trigram models were found to perform as well as or better than the finite-state language model. The more restrictive trigram and network models of language produce better recognition accuracy when all correct words are actually hypothesized, but strong constraints can degrade performance when many correct words are missing from the parser input.