How to detect grammatical errors in a text without parsing it

Abstract

The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a first-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text. This may seem to be an intuitively implausible and theoretically inadequate model of natural language syntax, but nevertheless it can successfully pinpoint most grammatical errors in a text. Several modifications to CLAWS have been explored. The resulting system cannot detect all errors in typed documents; but then neither do far more complex systems, which attempt a full parse, requiring much greater computation.