# Web-scale Surface and Syntactic n-gram Features for Dependency Parsing

arXiv: https://arxiv.org/abs/1502.07038 · HTML: https://mlsanity.com/paper/1502.07038/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2015.

## Built on by (most cited first, 1 of 1)

- [A Topological Approach to Compare Document Semantics Based on a New Variant of Syntactic N-grams](https://mlsanity.com/paper/2103.05135/) (2021)

## Abstract (arXiv)

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 n-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 n-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.