Web-scale Surface and Syntactic n-gram Features for Dependency Parsing
Table 2: lth uas (MSTParser) on the wsj dev and test set, and English Web Treebank (ewt) answers (ans), newsgroups (ngs), and reviews (rev) test set for the baseline (base), Web1T (web), Google Books (bks), Syntactic (syn), and combined (bks + syn) feature sets. All results are statistically significant improvements over the baseline.
['lth', 'mst', 'web', 'bks', 'syn', 'bks syn', '% Δ mst']
All of our features perform very similarly to one another: each feature set in isolation provides a roughly 0.5% uas improvement over the baseline parser on the wsj development and test sections. On the out-of-domain web treebank, surface and syntactic features each improve over the baseline by an average of roughly 0.8 – 1.0% on the test sets. All of our results are also statistically significant improvements over the baseline. For almost every pos tag, the combined system outperforms the baseline and makes equal or fewer errors than either the surface or syntactic n-gram features in isolation. Syntactic n-grams are worse relative to surface n-grams on noun, adjectival, and prepositional parts of speech – constructions which are known to be difficult to parse. parser are less effective. In comparison, surface n-grams are worse on conjunctive and verbal parts of speech, suggesting that the localized nature of these features is less useful for the idiosyncrasies of coordination representations and longer-range subject/object relationships.