  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.
