Abstract

Recognizing shallow linguistic patterns, such as ba-sic syntactic relationships between words, is a com~ mon task in applied natural language and text pro-(:essing. Tile common practice for approaching this task is by tedious manual definition of possible pat-tern structures, often in the h)rm of regular expres-sions or finite automata. This paper presents a novel memory-based learning method that recognizes shal-low patterns in new text based on a bracketed train-ing corpus. The training data are stored as-is, in efficient suttix-tree data structures. Generalization is performed on-line at recognition time by compar-ing subsequences of the new text to positive and negative evidence in the corIms. This way, no in-formation in tit(; training is lost, as can happen in other learning systems that construct a single gen-eralized model at the time of training. The paper presents experimental results for recognizing noun phrase, subject-verb and verb-object patterns in l!]n-glish. Since the learning approach enables easy port-ing to new domains, we plan to apply it to syntac-tic patterns in other languages and to sub-language patterns for information extraction. 1

Citations