Neural Approaches for Data Driven Dependency Parsing in Sanskrit
- URL: http://arxiv.org/abs/2004.08076v1
- Date: Fri, 17 Apr 2020 06:47:15 GMT
- Title: Neural Approaches for Data Driven Dependency Parsing in Sanskrit
- Authors: Amrith Krishna, Ashim Gupta, Deepak Garasangi, Jivnesh Sandhan,
Pavankumar Satuluri, Pawan Goyal
- Abstract summary: We evaluate four different data-driven machine learning models, originally proposed for different languages, and compare their performances on Sanskrit data.
We compare the performance of each of the models in a low-resource setting, with 1,500 sentences for training.
We also investigate the impact of word ordering in which the sentences are provided as input to these systems, by parsing verses and their corresponding prose order.
- Score: 19.844420181108177
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data-driven approaches for dependency parsing have been of great interest in
Natural Language Processing for the past couple of decades. However, Sanskrit
still lacks a robust purely data-driven dependency parser, probably with an
exception to Krishna (2019). This can primarily be attributed to the lack of
availability of task-specific labelled data and the morphologically rich nature
of the language. In this work, we evaluate four different data-driven machine
learning models, originally proposed for different languages, and compare their
performances on Sanskrit data. We experiment with 2 graph based and 2
transition based parsers. We compare the performance of each of the models in a
low-resource setting, with 1,500 sentences for training. Further, since our
focus is on the learning power of each of the models, we do not incorporate any
Sanskrit specific features explicitly into the models, and rather use the
default settings in each of the paper for obtaining the feature functions. In
this work, we analyse the performance of the parsers using both an in-domain
and an out-of-domain test dataset. We also investigate the impact of word
ordering in which the sentences are provided as input to these systems, by
parsing verses and their corresponding prose order (anvaya) sentences.
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