An N-グラム is a statistical 言語モデル that represents a contiguous sequence of n items (usually words or characters) from a given sample of text or speech. In 自然言語処理 (NLP) and computational linguistics, N-grams are used to analyze and model the structure of language, providing a method to predict the likelihood of a sequence of words occurring in a given context.
の値は n シーケンス内のアイテムの数を決定します：
- ユニグラム： (n=1) single words, e.g., “the”, “cat”.
- バイグラム： (n=2) pairs of consecutive words, e.g., “the cat”, “cat sat”.
- トリグラム： (n=3) sequences of three consecutive words, e.g., “the cat sat”.
By analyzing N-grams, models can capture local context and dependencies between words, which is essential for various NLP tasks such as text classification, language modeling, and 機械翻訳. For instance, a bigram model could help predict the next word based on the previous word, which enhances the understanding of language patterns.
N-gramは、検索エンジンなどのアプリケーションでよく使用される。 音声認識 systems, and predictive text input. However, while they are powerful, they also have limitations, such as requiring large amounts of data to be effective and the inability to capture long-range dependencies in language. Overall, N-grams are foundational in the field of NLP, serving as a building block for more complex models.