Yara Parser: A Fast and Accurate Dependency Parser

TL;DR

Yara Parser is a new dependency parser that combines high accuracy with fast processing speeds, suitable for various NLP applications and adaptable to different datasets.

Contribution

It introduces a fast, accurate, open-source dependency parser based on the arc-eager algorithm with beam search, offering flexibility and high performance.

Findings

Achieves 93.32% unlabeled accuracy on WSJ test set

Processes 4000 sentences per second in greedy mode

Processes 45 sentences per second with optimized accuracy settings

Abstract

Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machine translation and knowledge acquisition. We introduce the Yara Parser, a fast and accurate open-source dependency parser based on the arc-eager algorithm and beam search. It achieves an unlabeled accuracy of 93.32 on the standard WSJ test set which ranks it among the top dependency parsers. At its fastest, Yara can parse about 4000 sentences per second when in greedy mode (1 beam). When optimizing for accuracy (using 64 beams and Brown cluster features), Yara can parse 45 sentences per second. The parser can be trained on any syntactic dependency treebank and different options are provided in order to make it more flexible and tunable for specific tasks. It is released with the Apache version 2.0 license and can be used for both commercial and academic purposes. The parser can be found at https://github.com/yahoo/YaraParser.