Bengali Verb Classification: How Machine Learning Aids Natural Language Processing for Low-Resource Languages

This article introduces an open-source project that uses machine learning and large language models for automatic Bengali verb classification, aiming to address the digital divide faced by low-resource languages (such as Bengali) in natural language processing (NLP). The project explores the technical path of combining traditional machine learning with pre-trained language models, verifies its effectiveness in the task of classifying transitive/intransitive verbs, and discusses its application value in scenarios like machine translation and educational technology, as well as its open-source contributions.

In today's era of rapid AI development, high-resource languages like English and Chinese dominate NLP research, but thousands of languages worldwide face a "digital divide" due to lack of annotated data and computing resources. Bengali, as the seventh most spoken language in the world (with approximately 270 million users), is a typical example of a low-resource language that urgently needs high-quality NLP technical support.

The project adopts a hybrid technical solution:

1. Traditional machine learning models: Using SVM, Random Forest, Naive Bayes, etc., relying on manually designed features (such as part-of-speech tagging, word form changes, context co-occurrence words, etc.) to capture the morphological and syntactic characteristics of Bengali verbs;
2. Application of large language models: Introducing BERT and its multilingual variants (mBERT, XLM-RoBERTa) for fine-tuning, leveraging the rich language representations of pre-trained models to verify the advantages of transfer learning in low-resource scenarios;
3. Key insights from feature engineering: Focusing on morphological features of Bengali verbs such as person/tense/aspect markers, co-occurrence with dative particles, semantic roles, and syntactic dependency positions.

The project built an annotated Bengali verb dataset covering genres like news, literature, and social media, using accuracy, precision, recall, and F1 score as evaluation metrics. Experimental results show that deep learning models combined with linguistic features achieved high classification accuracy on the test set, significantly outperforming baseline methods.

The practical application value of this research includes:

1. Machine translation: Improving the fluency of translated texts;
2. Speech recognition: Enhancing syntactic parsing to improve transcription accuracy;
3. Educational technology: Providing intelligent grammar checking tools for Bengali learners;
4. Content analysis: Supporting sentiment recognition in social media monitoring and public opinion analysis.

As an open-source project, it provides code implementations, partial datasets, and pre-trained model weights, offering valuable infrastructure to the Bengali NLP community and lowering the threshold for subsequent research. It also demonstrates an effective way to accumulate annotated data for low-resource languages through crowdsourcing collaboration.

The Bengali verb classification project embodies the direction of efforts toward AI technology inclusiveness. Applying advanced machine learning technology to low-resource languages not only pushes the boundaries of linguistic research but also brings technological dividends to billions of users. True AI progress should benefit every language and community.