2 research outputs found
Web-scale Surface and Syntactic n-gram Features for Dependency Parsing
We develop novel first- and second-order features for dependency parsing
based on the Google Syntactic Ngrams corpus, a collection of subtree counts of
parsed sentences from scanned books. We also extend previous work on surface
-gram features from Web1T to the Google Books corpus and from first-order to
second-order, comparing and analysing performance over newswire and web
treebanks.
Surface and syntactic -grams both produce substantial and complementary
gains in parsing accuracy across domains. Our best system combines the two
feature sets, achieving up to 0.8% absolute UAS improvements on newswire and
1.4% on web text
Dependency Language Models for Transition-based Dependency Parsing
In this paper, we present an approach to improve the accuracy of a strong
transition-based dependency parser by exploiting dependency language models
that are extracted from a large parsed corpus. We integrated a small number of
features based on the dependency language models into the parser. To
demonstrate the effectiveness of the proposed approach, we evaluate our parser
on standard English and Chinese data where the base parser could achieve
competitive accuracy scores. Our enhanced parser achieved state-of-the-art
accuracy on Chinese data and competitive results on English data. We gained a
large absolute improvement of one point (UAS) on Chinese and 0.5 points for
English.Comment: Accepted by IWPT 201