mlsanityData to 7 Oct 2026

Yara Parser: A Fast and Accurate Dependency Parser

arXiv 1503.06733 ↗First posted 23 Mar 2015cs.CLcited by 110

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Abstract (arXiv)

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.

Key facts. Cited by 110 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2015.

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Citing papers per year: 2021: 4; 2022: 12; 2023: 18; 2024: 33; 2025: 31; 2026: 12.

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  30. 2023NAIST-SIC-Aligned: an Aligned English-Japanese Simultaneous Interpretation Corpuscited by 2
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