Text Representation
Text representation refers to the process of converting text data into a numerical format that can be easily processed by machine learning algorithms. This is an important step in natural language processing and text analytics, as most machine learning algorithms require numerical input. Text representation techniques include bag-of-words, term frequency-inverse document frequency (TF-IDF), word embeddings, and topic modeling. Bag-of-words represents text as a collection of words, ignoring grammar and word order. TF-IDF assigns weights to words based on their frequency in a document and their rarity across all documents. Word embeddings represent words as dense vectors in a high-dimensional space, capturing semantic relationships between words. Topic modeling identifies latent topics in a corpus of text, allowing for the discovery of underlying themes and patterns.
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