The Bag-of-Words (BoW) model represents text as a collection of word frequencies, ignoring grammar. It is a baseline method for tasks like text classification.

Text summarization condenses a document while retaining its key information. It can be extractive (selecting key sentences) or abstractive (generating new text).

POS (Part-of-Speech) tagging assigns grammatical labels (e.g., noun, verb) to words in a sentence. It aids in understanding sentence structure.

Stemming reduces words to their root form (e.g., "running" to "run"), while lemmatization maps words to their base form or lemma (e.g., "better" to "good"). Both are used for text normalization in NLP.

Sentiment analysis evaluates the emotional tone of text (positive, negative, neutral) using techniques like lexicon-based methods or machine learning models.

Text generation uses language models like GPT to create coherent and contextually relevant text based on input prompts.

Semantic analysis focuses on extracting meaning from text by analyzing the relationships between words. Tasks include sentiment analysis and text similarity.

Word embeddings, like Word2Vec and GloVe, are representations of words in vector space, capturing semantic relationships. They enhance the performance of models in tasks like text similarity and sentiment analysis.

Named Entity Recognition (NER) identifies and classifies entities in text, such as names, dates, locations, and organizations. For example, in "Apple launched a new iPhone," NER tags "Apple" as an organization.

Text classification assigns predefined categories to text data, such as spam filtering, email categorization, and topic labeling. Algorithms like Naive Bayes and SVM are commonly used.

Stop words are commonly used words (e.g., "the," "is," "and") that are often removed during text preprocessing to focus on meaningful words. Removing stop words enhances efficiency in text analysis.

Tokenization is the process of breaking down text into smaller units, such as words or phrases, known as tokens. It is a foundational step in NLP tasks like text preprocessing, sentiment analysis, and machine translation.

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model developed by Google. It uses a transformer architecture to understand the context of words in a sentence.

Key applications of NLP include:

• Sentiment analysis
• Chatbots and virtual assistants
• Machine translation (e.g., Google Translate)
• Text summarization
• Speech recognition (e.g., Siri, Alexa)
• Named entity recognition (NER)

Sequence-to-sequence modeling is a framework where the input and output are sequences. It is used in tasks like machine translation and text summarization, powered by architectures like RNNs and transformers.

• BERT: Bidirectional and excels in understanding context.
  
  
• GPT: Generative and focuses on text generation. 

Both are based on transformer architecture but optimized for different tasks.

• Supervised learning uses labeled data for tasks like text classification and sentiment analysis.
• Unsupervised learning uses unlabeled data for tasks like topic modeling and clustering.
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NLP (Natural Language Processing) is a branch of artificial intelligence focused on enabling computers to understand, interpret, and respond to human language in a meaningful way. It combines techniques from linguistics, computer science, and machine learning to process and analyze text or speech data.

A language model predicts the likelihood of a sequence of words. Modern language models like BERT, GPT, and XLNet excel in tasks such as text generation, summarization, and translation.

Machine translation converts text from one language to another using techniques like sequence-to-sequence models and transformers.

Word clouds visualize the most frequent words in text data. They are commonly used for exploratory text analysis.

Popular NLP libraries include:

• NLTK (Natural Language Toolkit)
• spaCy
• TextBlob
• Hugging Face Transformers
• Stanford NLP

Attention mechanisms allow models to focus on important parts of the input sequence, improving tasks like machine translation and text summarization.

Chatbots use NLP techniques like intent recognition, entity extraction, and dialog management to engage in meaningful conversations with users.

A transformer is an architecture that uses self-attention mechanisms to handle sequential data. Models like BERT and GPT are built on transformers.