Problems with n-Gram Models
Manas Rath
Principal Software Engineering Manager , Gen AI, LLM Leader @ Microsoft| PGP Texas Macomb in AIML | AIOPS | MLOPS, Network Automation, Product Engineering, Microsoft Certified AI Specialist
Problems with n-Gram Models
n-Gram models, while a fundamental tool in natural language processing, have certain limitations that can affect their performance in various tasks. These limitations arise from the underlying assumptions and statistical nature of the models.
Example : If you ask a question how many R are there in word "Strawberry", at times AI models responds 1 or 2.
What is the Reason for the issue
1. Data Sparsity
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2. Lack of Contextual Understanding
3. Long-Range Dependencies
4. Data Smoothing Techniques
5. Computational Complexity
To address these limitations, researchers have explored various techniques, including neural network-based models (e.g., recurrent neural networks, transformer models), statistical machine translation techniques, and hybrid approaches that combine n-gram models with other techniques. These advancements have significantly improved the performance of natural language processing systems in tasks such as machine translation, speech recognition, and text generation.