# DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit

arXiv: https://arxiv.org/abs/2310.09501 · HTML: https://mlsanity.com/paper/2310.09501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [Nested Named Entity Recognition with Partially-Observed TreeCRFs](https://mlsanity.com/paper/2012.08478/) (2020)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition](https://mlsanity.com/paper/2110.07480/) (2021)
- [Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing](https://mlsanity.com/paper/2203.04665/) (2022)
- [Improving Continual Relation Extraction through Prototypical Contrastive Learning](https://mlsanity.com/paper/2210.04513/) (2022)
- [Itihasa: A large-scale corpus for Sanskrit to English translation](https://mlsanity.com/paper/2106.03269/) (2021)
- [TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer](https://mlsanity.com/paper/2210.11753/) (2022)
- [KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction](https://mlsanity.com/paper/2106.00459/) (2021)
- [Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach](https://mlsanity.com/paper/2209.00243/) (2022)
- [Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds using Paraphrases in a Neural Model](https://mlsanity.com/paper/1803.08073/) (2018)
- [BERT(s) to Detect Multiword Expressions](https://mlsanity.com/paper/2208.07832/) (2022)
- [A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit](https://mlsanity.com/paper/2208.10310/) (2022)
- [Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphs](https://mlsanity.com/paper/2310.07848/) (2023)
- [Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation](https://mlsanity.com/paper/1809.06748/) (2018)

## Built on by (most cited first, 1 of 1)

- [Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?](https://mlsanity.com/paper/2511.08145/) (2025)

## Abstract (arXiv)

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