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…
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,…
Recurrent Neural Networks (RNNs) are a special kind of deep learning model. They are great at handling data that comes in a sequence, like time series data. Unlike regular neural networks, RNNs remember what came before to help with the current task. This makes them perfect for tasks like understanding language, recognizing speech, and predicting…
