uznlp-uz/uz_edbench

Uzbek Medical Entity Benchmark (UZ-EDBench)

📌 Description

UZ-EDBench is a structured Uzbek-language dataset designed for medical entity recognition and classification in a low-resource setting. The dataset is distributed in TSV format and consists of annotated tokens and domain-specific entity labels.

It includes:

• a main annotated corpus (UZ-EDBench.tsv)
• a structured list of medical specialists (UZ-EDBench.Doctors.tsv)

This dataset addresses the lack of:

• Uzbek medical NLP benchmarks
• annotated corpora for clinical entity extraction
• structured taxonomies of medical specialists

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🧠 Task Definition

The dataset supports:

1. Named Entity Recognition (NER)

• Input: tokenized Uzbek text
• Output: entity labels (BIO format)

2. Entity Classification

• Input: token / span
• Output: entity type (medical category)

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📊 Dataset Structure

🔹 Main File: UZ-EDBench.tsv

• Format: TSV (tab-separated)
• Each row = one token
• Sentence boundaries may be separated by empty lines

Typical format:

token	label
Bemor	O
kardiolog	B-DOCTOR_TYPE
qabuliga	O
keldi	O

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🔹 Auxiliary File: UZ-EDBench.Doctors.tsv

This file contains structured information about medical specialists (doctor types) used in annotation.

Typical structure:

doctor_type	description
kardiolog	Yurak kasalliklari bo‘yicha mutaxassis
nevrolog	Asab tizimi mutaxassisi

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🏷 Tagset (Entity Labels)

The dataset uses a domain-specific BIO tagging scheme.

🔹 Core Medical Entities

Tag	Description
B-DISEASE / I-DISEASE	Kasallik nomlari
B-SYMPTOM / I-SYMPTOM	Belgilar (simptomlar)
B-DRUG / I-DRUG	Dori vositalari
B-TREATMENT / I-TREATMENT	Davolash usullari
B-TEST / I-TEST	Tibbiy tekshiruvlar
B-ANATOMY / I-ANATOMY	Tana qismlari

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🔹 Doctor Types (Shifokor turlari)

Tag	Description
B-DOCTOR_TYPE / I-DOCTOR_TYPE	Tibbiy mutaxassislik

Examples include:

• kardiolog
• terapevt
• nevrolog
• pediatr
• jarroh
• dermatolog

The full list is provided in: 👉 UZ-EDBench.Doctors.tsv

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🔹 BIO Tagging Scheme

Tag	Meaning
B-XXX	Beginning of entity
I-XXX	Inside entity
O	Outside entity

Example:

kardiolog  B-DOCTOR_TYPE
shifokor   I-DOCTOR_TYPE

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🧾 Example

Token        Label
Bemor        O
nevrolog     B-DOCTOR_TYPE
qabuliga     O
bosh         B-ANATOMY
og‘rig‘i     B-SYMPTOM
bilan        O
keldi        O

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📏 Evaluation Protocol

Recommended metrics:

• Precision
• Recall
• F1-score (entity-level)
• Token-level accuracy

Evaluation should follow the CoNLL NER standard.

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📊 Data Splits

Predefined splits are not included.

Recommended split:

• Train: 80%
• Validation: 10%
• Test: 10%

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🎯 Use Cases

• 🏥 Uzbek medical NER systems
• 🤖 Fine-tuning transformer models (BERT, RoBERTa, Qwen, etc.)
• 📊 Clinical text mining
• 🧠 Healthcare AI assistants
• 🔍 Information extraction from Uzbek medical text

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UZ-EDBench

This repository contains two tab-separated subsets:

• default: the main triage benchmark in UZ-EDBench.tsv
• doctors: the doctor label reference table in UZ-EDBench.Doctors.tsv

⚙️ Loading the Dataset

from datasets import load_dataset

main = load_dataset("ruhilloalaev/uz_edbench", name="default")
doctors = load_dataset("ruhilloalaev/uz_edbench", name="doctors")

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⚠️ Notes

• Data is in Uzbek (Latin script)
• Format: TSV (tab-separated)
• Domain: medical / healthcare

Text may include:

• morphological variation
• domain-specific terminology
• spelling inconsistencies

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📜 License

This dataset is released under the CC-BY-4.0 License.