Abstract
We present a comparative error analysis of two parsers - MALT and MST on Telugu Dependency Treebank data. MALT and MST are currently two of the most dominant data-driven dependency parsers. We discuss the performances of both the parsers in relation to Telugu language. We also talk in detail about both the algorithmic issues of the parsers as well as the language specific constraints of Telugu. The purpose is, to better understand how to help the parsers deal with complex structures, make sense of implicit language specific cues and build a more informed Treebank.
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Notes
- 1.
MALT version 1.8.1.
- 2.
MST version 0.5.0.
- 3.
- 4.
LAS – Labeled Attachment Score.
- 5.
UAS – Unlabeled Attachment Score.
- 6.
LS - Labeled Score.
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Acknowledgment
We thank Riyaz Ahmad Bhat, Vigneshwaran Muralidharan and Irshad Ahmad Bhat for their assistance and comments that greatly improved the manuscript.
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Kanneganti, S., Chaudhry, H., Misra Sharma, D. (2018). Comparative Error Analysis of Parser Outputs on Telugu Dependency Treebank. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_28
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