N-Gram Language Models N-gram language models are a powerful type of statistical model used for natural language processing (NLP). N-grams are sequences of...
N-Gram Language Models
N-gram language models are a powerful type of statistical model used for natural language processing (NLP). N-grams are sequences of n words, and these models learn to predict the next word in a sequence based on the n preceding words.
For example, with a language model trained on a corpus of books, the next word "the" could be predicted based on the preceding words "a book," "a chapter," and "the." N-gram models are often used for tasks such as language translation, sentiment analysis, and text summarization.
Smoothing Techniques
Smoothing techniques are a set of methods used to improve the quality of text data by removing rare or noisy words. These techniques can help to reduce overfitting and improve the performance of language models.
There are two main types of smoothing techniques: statistical and rule-based smoothing. Statistical smoothing involves modeling the probability of a word based on its co-occurrence with other words in the language. Rule-based smoothing involves using specific rules or patterns to identify and remove rare or noisy words.
By using smoothing techniques, we can improve the quality of text data and make it more suitable for training language models