Natural Language Processing for Bengali: Challenges and Solutions

Bengali, spoken by over 230 million people worldwide, is the seventh most spoken language on the planet, yet it remains significantly underserved by mainstream natural language processing tools. Most NLP research and tooling is built around English, leaving languages with complex morphology, non-Latin scripts, and limited annotated corpora far behind. At our AI services division, we have invested heavily in building Bengali NLP capabilities, and this article shares the technical landscape, challenges, and practical solutions we have developed.

Tokenization: The First Hurdle

Tokenization in Bengali is deceptively complex. Unlike English, Bengali lacks consistent spacing conventions around punctuation, uses compound characters called yukta-akkhors, and frequently merges particles with root words. Whitespace-based splitting produces tokens that conflate morphologically distinct units. Subword tokenizers such as SentencePiece and Byte-Pair Encoding trained on large Bengali corpora provide better coverage, but they require careful corpus curation to avoid learning noisy segments from web-scraped data. We curate corpora from Prothom Alo, Bangla Academy publications, and government documents to ensure representative training material.

Morphological Analysis

Bengali is a morphologically rich language with extensive inflectional and derivational forms. A single verb root can generate dozens of surface forms through tense, mood, person, and honorific markers. Stemming algorithms designed for English perform poorly here. Finite-state transducers and neural morphological analyzers trained on annotated datasets such as the IIIT Hyderabad Bengali morphological corpus provide more reliable lemmatization. Proper lemmatization dramatically improves downstream tasks like search and topic modeling by collapsing variant forms into canonical entries.

Named Entity Recognition for Bangla

Named entity recognition in Bengali faces the absence of capitalization cues that English NER models rely on heavily. Person names, locations, and organizations must be identified purely from context and morphological patterns. Transformer-based models like BanglaBERT and multilingual BERT fine-tuned on annotated Bengali NER datasets achieve F1 scores above 85%, but performance degrades on informal text from social media where code-switching between Bengali and English is rampant. We address this by augmenting training data with synthetic code-switched examples and using character-level embeddings that capture script transitions.

Handling Code-Switching

Bangladeshi digital content frequently mixes Bengali script, romanized Bengali, and English within a single sentence. A customer review might read: "Product ta really bhalo, delivery fast chilo." Processing such text requires language identification at the token level, transliteration normalization, and models robust to mixed-script input. We maintain a romanized-to-Bengali transliteration module that normalizes input before feeding it to downstream models, significantly boosting accuracy on real-world data.

Sentiment Analysis for Bengali

Sentiment analysis in Bengali must account for sarcasm expressed through particles like "নাকি" and "তো", negation patterns that differ from English, and culturally specific expressions. A phrase that appears positive on the surface may carry negative sentiment through irony. We train sentiment classifiers on a curated dataset of product reviews, social media comments, and news editorials with fine-grained annotation guidelines. Transfer learning from multilingual models provides a strong initialization, but domain-specific fine-tuning on Bengali data is essential for production-grade accuracy.

Building Bengali NLP Infrastructure

Beyond individual tasks, the ecosystem requires foundational infrastructure: large-scale pre-trained language models, standardized evaluation benchmarks, and open annotated datasets. We contribute to open-source Bengali NLP through cleaned corpora, pre-trained embeddings, and evaluation scripts. Products like Bondorix integrate Bengali text understanding to serve local market needs. The Bangladeshi AI community is growing rapidly, and collaborative efforts between universities, startups, and enterprises are accelerating progress.

If your business needs to process Bengali text at scale—whether for customer feedback analysis, document understanding, or conversational interfaces—contact us to explore our Bengali NLP solutions. The language deserves first-class computational treatment, and we are committed to building the tools that make it possible.