Discover the key limitations of n-gram models in natural language processing (NLP), focusing on how they struggle with unseen sequences and exponential growth as n increases.
Table of Contents
Question
What is one of the major issues faced by n-gram models?
A. They are too slow for large-scale applications
B. They cannot handle text in languages other than English.
C. They have a fixed history window.
D. They grow exponentially in size as n increases and cannot predict unseen sequences.
Answer
D. They grow exponentially in size as n increases and cannot predict unseen sequences.
Explanation
N-gram models, used in natural language processing (NLP), estimate the likelihood of a word based on the previous n-1 words. While they are simple and effective for certain tasks, they come with significant drawbacks:
- Exponential Growth: As the value of n increases, the number of possible n-grams grows exponentially. This means that for larger values of n, the model requires much more memory and computational power to store and process all possible combinations of words. This makes scaling n-gram models for large datasets impractical.
- Inability to Handle Unseen Sequences: N-gram models rely on fixed sequences from the training data. If a specific sequence of words has not appeared during training, the model cannot predict it. This is a major flaw, as natural language is inherently dynamic, with many potential word combinations that may not exist in the training data.
- Fixed History Window: Although this is not as significant a problem as the other options, it does limit the model’s ability to account for long-range dependencies in text. N-gram models are bound by a fixed window of previous words, meaning they can’t remember beyond the n-1 length, which hampers context understanding.
In contrast to modern deep learning techniques like Transformers, which can capture long-term dependencies and handle unseen sequences effectively, n-gram models are limited and inefficient for more complex NLP tasks.
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