grambased

Grambased refers to methods that treat grams (n-grams) as the primary units for analyzing text. It includes both word-level n-grams, sequences of consecutive words, and character-level n-grams, sequences of characters. Grambased approaches typically extract a set of n-grams from a corpus and represent documents as vectors of n-gram counts or weights, often using TF-IDF. They then train classifiers or estimate language models from these representations. Word-level n-grams capture local word order and topical signals, while character-level n-grams can be more robust to misspellings and work across languages with rich morphology.

Common applications include text classification, language identification, spam detection, author attribution, and information retrieval. Historically, grambased models (unigrams through trigrams) were widely used for language modeling and classification before the advent of neural models. They rely on statistical estimates of n-gram probabilities, typically with smoothing (Laplace, Good-Turing) to handle unseen sequences.

Advantages of grambased methods include simplicity, interpretability, and reasonable performance on moderate datasets without extensive linguistic resources. They can be easy to implement and reason about. Limitations include high dimensionality and sparsity for higher-order n-grams, limited ability to capture long-range dependencies, and sensitivity to noise or typos. They may underperform on tasks requiring deeper semantic understanding.

Grambased is distinct from grammar-based (rule-based) approaches that rely on explicit syntactic or semantic rules rather than statistical n-gram statistics. See also: n-gram, language model, TF-IDF.