Handling ambiguity: HMMs vs. neural network models
Probabilistic models like Hidden Markov Model (HMMs) handle ambiguity by assigning probabilities to sequences and ...
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Handling ambiguity: HMMs vs. neural network models
Probabilistic models like Hidden Markov Model (HMMs) handle ambiguity by assigning probabilities to sequences and selecting the most likely interpretation based on local context (e.g., previous words or tags). However, they rely on simplifying assumptions (like the Markov property), which limits their ability to capture long-range dependencies.
In contrast, neural network–based models (e.g., deep learning/LLMs) handle ambiguity using distributed representations and broader context. They can consider entire sentences (or even paragraphs), allowing them to better resolve ambiguous words based on meaning and usage patterns.
👉 In short:
HMMs resolve ambiguity through probability over limited context
Neural models resolve ambiguity through context-rich, learned representations
