👉 Extraction math involves using mathematical techniques to identify and extract meaningful patterns or structures from large datasets, often in the context of natural language processing (NLP) and machine learning. This process typically employs statistical methods, such as frequency analysis, to uncover recurring patterns or elements within text data. For example, in text mining, extraction math might involve calculating the frequency of words or phrases to identify common themes or topics. Techniques like TF-IDF (Term Frequency-Inverse Document Frequency) are used to weigh the importance of words by considering their frequency in a document and across a corpus, enabling the extraction of key terms that are both frequent within specific texts and rare across the entire dataset. This helps in summarizing large volumes of text, generating insights, or building more efficient models for tasks like sentiment analysis or topic modeling.
