Preserving the Endangered Hawrami Dialect of Kurdish through Ensemble Machine Learning Text Classification

Alapfogalmak

Ensemble machine learning models can effectively classify Hawrami text, a Kurdish dialect at risk of endangerment, to aid in its preservation.

Kivonat

This paper presents an approach to text classification for the Hawrami dialect of Kurdish, which is classified as an endangered language. The researchers collected a dataset of 6,854 Hawrami articles from two online sources and labeled them into 15 categories by native speakers.

The key highlights and insights are:

1. Hawrami is a Kurdish dialect that faces challenges due to data scarcity and the gradual loss of speakers. The classification of Hawrami as a language or dialect is an ongoing debate among scholars.

2. The researchers employed web scraping and robotic process automation techniques to collect the Hawrami text data. They then preprocessed the data, including normalization, stop-word removal, and balancing techniques to address the imbalanced dataset.

3. Four machine learning algorithms were evaluated for the text classification task: K-Nearest Neighbor (KNN), Linear Support Vector Machine (Linear SVM), Logistic Regression (LR), and Decision Tree (DT). The models were trained and tested on both the imbalanced and balanced datasets.

4. The results show that the Linear SVM model achieved the highest accuracy of 96% and outperformed the other approaches. The balanced dataset scenarios also improved the overall performance of the models, particularly for the minor classes.

5. The researchers provided insights into the decision-making process of the classifiers using the LIME interpreter and found that the preprocessing stage played a crucial role in the models' performance.

6. The authors conclude that ensemble machine learning techniques can effectively classify Hawrami text, which can contribute to the preservation of this endangered Kurdish dialect. They also suggest future work on improving preprocessing techniques and applying feature selection methods to enhance the models' performance.

Statisztikák

The dataset contains 6,854 Hawrami articles, with the politics category having the most entries (54.69%) and the painting category having the least (0.01%).
The average number of words per article is 343.29, with the language category having the highest average (684.14) and the sport category having the lowest (106.8).

Idézetek

"Hawrami is spoken by the people of Hawraman, a place that traverses the border of Iran and Iraq."
"UNESCO listed it as 'definitely endangered' because it seems it is gradually losing speakers and its online resources are not growing as expected."
"The Linear SVM classifier scored the highest accuracy of 96% in the first two scenarios."

Főbb Kivonatok

Shifting from endangerment to rebirth in the Artificial Intelligence Age: An Ensemble Machine Learning Approach for Hawrami Text Classification

by Aram Khaksar... : arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16884.pdf

Mélyebb kérdések

How can the preprocessing techniques be further improved to enhance the performance of the text classification models for low-resource languages like Hawrami?

To enhance the performance of text classification models for low-resource languages like Hawrami, preprocessing techniques can be further improved in several ways:

Advanced Text Normalization: Beyond basic normalization, implementing techniques such as lemmatization and stemming can help reduce words to their base forms, thereby decreasing dimensionality and improving model performance. This is particularly useful in low-resource settings where vocabulary diversity can lead to sparsity.

Contextual Stopword Removal: Instead of using a static list of stopwords, a contextual approach could be employed. This involves analyzing the frequency and relevance of words within specific categories to dynamically adjust the stopword list, ensuring that only truly irrelevant words are removed.

Handling Imbalanced Data: While the study employed under-sampling and SMOTE, further techniques such as adaptive synthetic sampling (ADASYN) could be explored. This method focuses on generating synthetic data for minority classes based on the distribution of the data, potentially leading to better representation of underrepresented categories.

Feature Selection Techniques: Implementing feature selection methods such as Recursive Feature Elimination (RFE) or using model-based feature importance (e.g., from tree-based models) can help identify and retain only the most relevant features, reducing noise and improving classification accuracy.

Utilizing Domain-Specific Knowledge: Engaging linguists and native speakers to identify domain-specific terms and phrases can enhance the preprocessing stage. This could involve creating specialized dictionaries or thesauri that reflect the unique characteristics of the Hawrami dialect.

Data Augmentation: Techniques such as back-translation or paraphrasing can be employed to artificially increase the size of the dataset. This is particularly beneficial for low-resource languages, as it can help mitigate the effects of data scarcity.

By implementing these advanced preprocessing techniques, the overall quality of the dataset can be improved, leading to enhanced performance of text classification models for Hawrami.

What other machine learning or deep learning approaches could be explored to address the challenges of data scarcity and imbalance in Hawrami text classification?

To address the challenges of data scarcity and imbalance in Hawrami text classification, several machine learning and deep learning approaches can be explored:

Transfer Learning: Utilizing pre-trained models such as BERT or its variants (e.g., mBERT for multilingual tasks) can significantly enhance performance. These models can be fine-tuned on the Hawrami dataset, leveraging knowledge from larger datasets in related languages, which can help overcome data scarcity.

Ensemble Learning: Combining multiple models through techniques like bagging and boosting can improve classification performance. For instance, using Random Forests or Gradient Boosting Machines can help in capturing complex patterns in the data while also addressing class imbalance.

Generative Adversarial Networks (GANs): GANs can be employed to generate synthetic text data for the minority classes. By training a generator to create realistic text samples, the model can help balance the dataset and improve classification accuracy.

Few-Shot Learning: Implementing few-shot learning techniques can be beneficial in scenarios where labeled data is scarce. Approaches like Prototypical Networks or Matching Networks can learn to classify new examples based on a few labeled instances, making them suitable for low-resource languages.

Multi-Task Learning: This approach involves training a model on multiple related tasks simultaneously, which can help improve generalization. For example, a model could be trained on both text classification and sentiment analysis tasks, sharing representations that can enhance performance on the primary classification task.

Active Learning: Implementing active learning strategies can help prioritize the labeling of the most informative samples. By iteratively selecting the most uncertain predictions for human annotation, the model can improve its performance with fewer labeled examples.

By exploring these advanced machine learning and deep learning approaches, researchers can better tackle the challenges posed by data scarcity and imbalance in Hawrami text classification.

Given the ongoing debate around the classification of Hawrami as a language or dialect, how could the insights from this study contribute to a better understanding of the linguistic relationships between Hawrami and other Kurdish dialects?

The insights from this study can significantly contribute to a better understanding of the linguistic relationships between Hawrami and other Kurdish dialects in several ways:

Empirical Data Collection: By compiling a substantial dataset of Hawrami texts and categorizing them, the study provides empirical evidence that can be used to analyze linguistic features, vocabulary, and syntax. This data can help linguists compare Hawrami with Sorani and Kurmanji, shedding light on their similarities and differences.

Text Classification as a Linguistic Tool: The application of text classification models can serve as a methodological framework for linguistic analysis. By identifying and categorizing texts based on linguistic features, researchers can gain insights into the unique characteristics of Hawrami and how they relate to other Kurdish dialects.

Highlighting Mutual Intelligibility: The study's findings regarding the classification accuracy of texts can provide evidence for or against the mutual intelligibility of Hawrami with other Kurdish dialects. If the models perform well across dialects, it may suggest a closer linguistic relationship than previously thought.

Facilitating Standardization Efforts: The insights gained from the study can inform ongoing discussions about the standardization of Kurdish dialects. By understanding the linguistic features that are common or distinct among the dialects, stakeholders can work towards creating a more unified approach to Kurdish language education and resources.

Encouraging Further Research: The study can serve as a catalyst for further research into the linguistic classification of Hawrami. By providing a foundation of data and analysis, it can inspire linguists to explore the historical, social, and political factors that influence the classification of Hawrami as a dialect or an independent language.

Promoting Cultural Awareness: By documenting and analyzing the Hawrami dialect, the study contributes to the preservation of cultural identity and heritage. This can foster greater appreciation and understanding of the linguistic diversity within the Kurdish language, encouraging efforts to support and revitalize endangered dialects.

In summary, the insights from this study can play a crucial role in advancing the understanding of the linguistic relationships between Hawrami and other Kurdish dialects, ultimately contributing to the broader field of Kurdish linguistics.