---
license: cc-by-4.0
language:
- kk
tags:
- duplicate
- text-similarity
- plagiarism-detection
- kazakh
- NLP
size_categories:
- 10K<n<100K
---
# Dataset Card for KazakhTextDuplicates

## Dataset Details

### Dataset Description

The **KazakhTextDuplicates** dataset is a collection of Kazakh-language texts containing duplicates with different levels of modification. The dataset includes **exact duplicates**, **contextual duplicates**, and **partial duplicates**, making it valuable for research in **text similarity, duplicate detection, information retrieval, and plagiarism detection**. 

- **Developed by:** Arailym Tleubayeva
- **Language(s) (NLP):** Kazakh (`kk`)
- **License:** CC-BY-4.0


## Uses

### Direct Use

This dataset can be used for:
- **Duplicate text detection**: Identifying identical or near-identical texts in large collections.
- **Plagiarism detection**: Detecting reworded or slightly modified texts.
- **Information Retrieval (IR)**: Improving search engines by training models to recognize near-duplicate documents.
- **Semantic Text Similarity (STS)**: Training NLP models to recognize different levels of text similarity.
- **Data Augmentation**: Generating paraphrased datasets for training models on low-resource Kazakh text data.

### Out-of-Scope Use

- Not suitable for **general language modeling** without additional preprocessing.
- Not designed for **machine translation** tasks.

## Dataset Structure

The dataset consists of **Kazakh-language texts** with **three types of duplicate labels**:

- **Exact duplicate**: The texts are identical.
- **Contextual duplicate**: The texts have been slightly modified but retain the same meaning.
- **Partial duplicate**: Only a portion of the text is similar.

### Columns:

| Column Name       | Description |
|------------------|-------------|
| `id`            | Unique document ID |
| `content`       | Original Kazakh text |
| `category`      | Text category (e.g., article, news, academic, etc.) |
| `language`      | Language identifier (`kk` for Kazakh) |
| `type_duplicate` | Type of duplicate (`exact`, `contextual`, `partial`) |
| `modified_content` | Modified version of the original text |

## Dataset Creation

### Curation Rationale

This dataset was created to address the **lack of publicly available Kazakh-language datasets** for duplicate detection and text similarity tasks. It is particularly useful for NLP applications involving **Kazakh text retrieval, plagiarism detection, and duplicate content filtering**.

### Source Data

The dataset consists of **curated text corpora** from:
- **Public Kazakh-language sources** (news articles, academic texts, Wikipedia, etc.).
- **Synthetic modifications** created using NLP-based transformations.

#### Data Collection and Processing

The dataset was processed using:
- **Text normalization** (lowercasing, punctuation removal).
- **Stopword removal** (Kazakh stopwords).
- **Duplication type classification** (exact/contextual/partial).
- **Data augmentation** for modified versions.

#### Who are the source data producers?

- Kazakh-language content producers (media, academia).
- Synthetic text generation tools.

### Annotations [optional]

#### Annotation process

The dataset was annotated using **automatic similarity detection models** and **manual verification**.

#### Who are the annotators?

The dataset was reviewed by **Kazakh-speaking NLP researchers** and **linguists**.

#### Personal and Sensitive Information

The dataset does **not** contain personal or sensitive data. Any potential **identifiable information was removed** during preprocessing.

## Bias, Risks, and Limitations

- **Bias in duplicate detection**: The dataset might favor certain duplicate structures over others.
- **Linguistic variation**: Limited to standard Kazakh; regional dialects may not be fully represented.
- **Machine-generated paraphrases**: Some `contextual duplicates` were generated using NLP models, which may introduce biases.

### Recommendations

Users should be aware of **potential biases** and perform **additional validation** when applying the dataset to real-world tasks.