DepNeCTI: Dependency-based Nested Compound Type Identification for
  Sanskrit
Jivnesh Sandhan Yaswanth Narsupalli Sreevatsa Muppirala Sriram Krishnan Pavankumar Satuluri Amba Kulkarni Pawan Goyal
Multi-component compounding is a prevalent phenomenon in Sanskrit, and
understanding the implicit structure of a compound's components is crucial for
deciphering its meaning. Earlier approaches in Sanskrit have focused on binary
compounds and neglected the multi-component compound setting. This work
introduces the novel task of nested compound type identification (NeCTI), which
aims to identify nested spans of a multi-component compound and decode the
implicit semantic relations between them. To the best of our knowledge, this is
the first attempt in the field of lexical semantics to propose this task.
  We present 2 newly annotated datasets including an out-of-domain dataset for
this task. We also benchmark these datasets by exploring the efficacy of the
standard problem formulations such as nested named entity recognition,
constituency parsing and seq2seq, etc. We present a novel framework named
DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the
performance of the best baseline with an average absolute improvement of 13.1
points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement
in inference efficiency. In line with the previous findings in the binary
Sanskrit compound identification task, context provides benefits for the NeCTI
task. The codebase and datasets are publicly available at:
https://github.com/yaswanth-iitkgp/DepNeCTI