N-gram matching is a technique focused on detecting the occurrences of fixed sets of pattern arrays embedded as sub-arrays within a larger input array. Instead of focusing on individual characters or semantic meaning, this method focuses on substring patterns. An example of splitting text into bigrams (sets of two words) or trigrams (sets of three words) illustrates how N-grams are formed for analysis.
N-gram based algorithms are highly efficient for handling large datasets. They are also highly efficient for quickly extracting data that involves large patterns. Their benefits include usefulness for detecting partial matches, identifying patterns, or finding key phrases, and the method is generally scalable. The technique can be used in projects like hashtag normalization.
However, N-gram matching does have limitations. It can be computationally expensive for very long strings or when high N-gram values are used. Importantly, N-gram matching offers limited semantic understanding compared to embedding-based approaches, as it prioritizes patterns over contextual meaning. Despite this limitation, it is a key component in fuzzy matching hybrid approaches, often combined with TF-IDF to preprocess text and improve contextual relevance.