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NeCTIS-Dataset

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metadata
language: san
license: apache-2.0
tags:
  - dependency-parsing
  - nested-compound-type-identification
  - lstm
  - nlp
  - sanskrit
  - pytorch

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

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

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:
      • AvyayΔ«bhava (Indeclinable)
      • BahuvrΔ«hi (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 β€” 1,189 β€” 4 (86)

Domain & Genre

  • NeCTIS: Philosophical, Literary, and Ayurvedic domains β†’ Prose

  • NeCTIS-OOD: Paurāṇic (epic literature) domain β†’ 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 

β”œβ”€β”€ README.md
β”œβ”€β”€ LICENSE
β”œβ”€β”€ No Context CSV files/
β”‚   β”œβ”€β”€ Combined.csv
β”‚   β”œβ”€β”€ test.csv
β”‚   β”œβ”€β”€ train.csv
β”‚   β”œβ”€β”€ dev.csv
β”‚   └── outofDomain.csv
β”œβ”€β”€ With Context CSV files/
β”‚   β”œβ”€β”€ Combined.csv
β”‚   β”œβ”€β”€ test.csv
β”‚   β”œβ”€β”€ train.csv
β”‚   β”œβ”€β”€ dev.csv
β”‚   └── 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