ngramanalyser

ngramanalyser is a software tool or library designed to extract and analyze n-grams from text. It supports both word-level and character-level n-grams and is commonly used in natural language processing, information retrieval, and linguistic research. The tool tokenizes input text, applies optional normalization such as lowercasing, accents removal, and punctuation handling, and can perform stopword removal or stemming. Users can specify a range of n values (for example, from 1 to 2 or 2 to 5), and the analyzer computes all n-grams within that range using a sliding window over the token sequence. The resulting data typically include frequency counts for each n-gram, relative frequencies, and the ability to extract the top-N most frequent n-grams. Some implementations offer additional features such as confidence weighting, min-supported thresholds, or filtering by length.

ngramanalyser outputs are designed to integrate with data pipelines and can be exported to common formats such as CSV or JSON for further analysis or visualization. It is used for language modeling, authorship attribution, stylometry, keyword extraction, and corpus linguistics, as well as for building features in text classification or search applications. Performance considerations include streaming processing to handle large corpora, memory-efficient counting, and parallel processing in multi-core environments.

Implementations vary across languages, but typical offerings are available in Python, Java, and JavaScript, with APIs that allow incremental text processing, batch analysis, and retrieval of frequency distributions or n-gram statistics. The concept of an n-gram analyzer dates to early work in statistical language modeling and remains a common building block in modern NLP toolkits.