Word embeddings are key in natural language processing (NLP). They change how machines understand text. These numeric forms of words in a lower-dimensional space hold the meaning and structure of language. This lets machines see how words relate and are similar. Word embeddings are vital for many NLP tasks. These include text classification, named entity…
In natural language processing, unigrams, bigrams, and n-grams are statistical models. They help predict the probability of word sequences. These models uncover patterns in text, aiding in tasks like language modeling and sentiment analysis. A unigram model looks at each word alone, ignoring the word before it. Bigrams, on the other hand, consider the word…
Named entity recognition (NER) is a way to extract information from text. It finds and sorts out key information in text called named entities. These entities are important subjects in a text, like names, places, companies, events, and products. NER helps machines understand and sort these entities. This is useful for tasks like text summarization,…
