Datasets:

sanganaka
/

NeCTIS-Dataset

	---
	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](https://arxiv.org/abs/2310.09501)

	---
	# 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\|2,5 Dvandva`. \|
	\|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
	```bibtex
	@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](http://arxiv.org/abs/2310.09501)

	Github Repository of [DepNeCTI](https://github.com/yaswanth-iitkgp/DepNeCTI)

	---
	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](https://arxiv.org/abs/2310.09501)

	---
	# 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\|2,5 Dvandva`. \|
	\|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
	```bibtex
	@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](http://arxiv.org/abs/2310.09501)

	Github Repository of [DepNeCTI](https://github.com/yaswanth-iitkgp/DepNeCTI)