Text Classification is a core NLP task where a model learns to assign predefined categories or labels to text data.

Tokenization is the process of breaking down text into individual units, typically words, for further processing.

Naive Bayes is a popular algorithm for text classification due to its simplicity, efficiency, and ability to handle high-dimensional feature spaces.

NER aims to identify and categorize specific types of entities, such as persons, organizations, locations, and dates, within text data.

F1-score is a commonly used metric in NLP for evaluating text classification models. It considers both precision and recall, providing a balanced measure of model performance.

Machine Translation involves translating text from one language to another, enabling communication and understanding across different linguistic boundaries.

LSTM is a type of recurrent neural network commonly used in sentiment analysis. It can learn long-term dependencies in text data, making it effective for capturing sentiment.

Text Summarization aims to create a concise and informative summary of a larger text, capturing its key points and essential information.

POS tagging assigns grammatical categories (e.g., noun, verb, adjective) to words in a sentence, providing insights into their syntactic structure.

Bag-of-Words (BOW) is a simple yet effective method for representing text data by converting it into a vector of word frequencies.

Question Answering (QA) systems aim to provide accurate and relevant answers to natural language questions posed by users.

Generative Adversarial Networks (GANs) are frequently employed for text generation tasks, as they can learn to generate realistic and coherent text.

Natural Language Inference (NLI) involves determining whether a hypothesis sentence can be inferred from a given premise sentence.

BERT (Bidirectional Encoder Representations from Transformers) is a widely used pre-trained language model that has achieved state-of-the-art results on various NLP tasks.

Topic Modeling aims to discover and extract the underlying topics or themes present in a collection of documents.