sanganaka/NeCTIS-Dataset

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

This repository contains the datasets proposed in the paper Sandhan et al., 2023

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Summary

The NeCTIS dataset is created for nested compound type identification in Sanskrit, focusing on multi-component compounds (especially with more than 2 components). It includes coarse and fine-grained semantic type annotations.

Key Features

• Two datasets:

  • NeCTIS: In-domain (Prose)
  • NeCTIS-OOD: Out-of-domain (Poetry)

• Annotations:

  • Coarse-level: 4 broad compound types:
    • Avyayībhava (Indeclinable)
    • Bahuvrīhi (Exocentric)
    • Tatpurusha (Endocentric)
    • Dvandva (Copulative)
  • Fine-grained: 86 detailed sub-types

Dataset Statistics

Dataset	#Nested Compounds	Train	Test	Dev	Compound Types
NeCTIS	17,656	12,431	3,493	2,405	4 (86)
NeCTIS-OOD	1,189	—	1,189	—	4 (86)

Domain & Genre

• NeCTIS: Philosophical, Literary, and Ayurvedic domains → Prose

• NeCTIS-OOD: Paurāṇic (epic literature) domain → Poetry

  • Poetry tends to include more novel, complex, and metrical compounds.

Annotation Process

• Funded by DeitY (2009–2012) as part of the Sanskrit-Hindi Machine Translation project
• Annotation done by 6 institutes, each with ~10 members across 3 expertise levels:
  • Junior Linguist (Master’s in Sanskrit)
  • Senior Linguist (Ph.D. in Sanskrit)
  • Professional Linguist (Professors)
• Multi-level quality checks and cross-institute validation
• Annotation guidelines based on Pāṇinian grammar and traditional commentaries.

For Detailed information refer to the original paper.

Files

├── README.md
├── LICENSE
├── No Context CSV files/
│   ├── Combined.csv
│   ├── test.csv
│   ├── train.csv
│   ├── dev.csv
│   └── outofDomain.csv
├── With Context CSV files/
│   ├── Combined.csv
│   ├── test.csv
│   ├── train.csv
│   ├── dev.csv
│   └── outofDomain.csv

Dive in the Dataset

With Context

• Total rows: 15,940 rows.
• Split: Train (69%), Test (18%), Dev (13%).
• train CSV: 11,000 rows.
• test CSV: 2,940 rows.
• dev CSV: 2,000 rows.
• The combined CSV: all above files merged, 15,940 rows total.
• These four CSV files include the following columns:
  Unnamed: 0, Raw_Tagged, Clean, Bio_tagged, Span_Tagged, Coarse_tag, Compound_lengths, Coarse_Span_Tagged.
• The out of Domain contains 1139 rows
• The out-of-domain dataset has an additional column, Book, and lacks the Compound_lengths column.

Column Name	Description
Unnamed: 0.1	Auto-generated row index by pandas when reading CSV, used as a unique identifier for each row.
Unnamed: 0	Another index column created during CSV operations, often redundant with Unnamed: 0.1.
Raw_Tagged	Original input text with embedded annotation tags marking nested compound components and their types. Tags use angled brackets < > and suffix codes to show component boundaries and types.
Clean	Cleaned, tokenized version of the text without any annotation tags or special characters, for plain reading.
Bio_tagged	BIO tagging scheme at the token level indicating compound boundaries: B-C = Beginning of a compound segment, I-C = Inside a compound segment, O = Outside any compound (non-compound token)
Span_Tagged	Token spans marking compound segments along with their labels. Format: start,end Label. Multiple spans are separated by |. For example, 0,2 BvS|2,5 Ds means tokens from index 0 to 2 form a BvS compound, tokens 2 to 5 form a Ds compound.
Coarse_tag	Coarse-grained compound type annotations embedded with original tagged segments, indicating linguistic compound types such as Bahuvrihi, Tatpurusha, or Dvandva.
Compound_lengths	List indicating the length (number of tokens) of each compound segment in the text. For example, [1, 1] means there are two compounds each one token long.
Coarse_Span_Tagged	Combines span indices with coarse compound type labels to specify the token ranges and their compound categories. For example: `0,2 Bahuvrihi
Book	Present in ood dataset. Informs about the book from which the sentence is taken.

Without Context

• Total rows: 15,940 rows.
• Split: Train (69%), Test (18%), Dev (13%).
• train CSV: 11,000 rows.
• test CSV: 2,940 rows.
• dev CSV: 2,000 rows.
• The combined CSV: all above files merged, 15,940 rows total.
• The out of Domain contains 1139 rows
• These five CSV files include the following columns:
  Unnamed: 0, Raw_Tagged, Clean, Bio_tagged, Span_Tagged, Coarse_tag, Coarse_Span_Tagged.

Column Name	Description
Unnamed: 0.1	Auto-generated index column, unique identifier for each row.
Unnamed: 0	Another index column created during CSV processing, often redundant with Unnamed: 0.1.
Raw_Tagged	Original text with embedded annotation tags marking nested compound components. Tags use angled brackets < > with suffix codes for component boundaries and types. Example: <sa-sarzapaM>BvS <tumburu-DAnya-vanyaM>Ds
Clean	Cleaned and tokenized text with no annotation tags, representing plain text tokens.
Bio_tagged	BIO scheme token-level tagging indicating compound boundaries: B-C = Beginning of a compound segment I-C = Inside a compound segment O = Outside any compound segment.
Span_Tagged	Token span annotations marking compound segments with labels. Format: start,end Label. Multiple spans are separated by |. For example, 0,2 BvS|2,5 Ds indicates tokens 0 to 2 form a BvS compound, and tokens 2 to 5 form a Ds compound.
Coarse_tag	Coarse-grained compound type annotations embedded in the original tagged text, indicating linguistic compound types such as Bahuvrihi, Tatpurusha, or Dvandva.
Coarse_Span_Tagged	Combines token span indices with coarse compound type labels, e.g., 0,2 Bahuvrihi|2,5 Dvandva specifies token ranges and their corresponding compound categories.

Citation

@misc{sandhan2023depnecti,
      title={DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit}, 
      author={Jivnesh Sandhan and Yaswanth Narsupalli and Sreevatsa Muppirala and Sriram Krishnan and Pavankumar Satuluri and Amba Kulkarni and Pawan Goyal},
      year={2023},
      eprint={2310.09501},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Original paper DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit

Github Repository of DepNeCTI