We investigate the effect of corpus size in combining supervised and unsupervised learning for two types of attachment decisions: relative clause attachment and prepositional phrase attachment. The supervised component is Collins' parser, trained on the Wall Street Journal. The unsupervised component gathers lexical statistics from an unannotated corpus of newswire text. We find that the combined system only improves the performance of the parser for small training sets. Surprisingly, the size of the unannotated corpus has little effect due to the noisiness of the lexical statistics acquired by unsupervised learning.
Atterer and Schütze REF investigated the effect of corpus size in combining supervised and unsupervised learning for two types of attachment decision; they found that the combined system only improved the performance of the parser for small training sets.
The Effect Of Corpus Size In Combining Supervised And Unsupervised Training For Disambiguation
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics - Poster Sessions
Computer Science