NLP : Important terms

Natural Language Processing (NLP) is a field with a wide range of important terms and concepts. Here are some key terms in NLP:

1. Corpus: A corpus is a large and structured collection of text documents used for linguistic analysis, training NLP models, and research.
2. Tokenization: Tokenization is the process of breaking text into individual units, such as words or subwords (subword tokenization), to facilitate analysis and processing.
3. Part-of-Speech (POS): POS tagging involves labeling each word in a text with its grammatical category, such as noun, verb, adjective, etc.
4. Stop Words: Stop words are common words (e.g., "and," "the," "is") that are often removed from text during preprocessing because they carry little semantic meaning.
5. Lemmatization: Lemmatization is the process of reducing words to their base or dictionary form (lemma), ensuring that different inflected forms of a word are treated as the same word.
6. Stemming: Stemming is a process of reducing words to their root or stem form, often by removing suffixes. It's a more aggressive simplification compared to lemmatization.
7. Syntax: Syntax refers to the structure of sentences and the rules governing the arrangement of words in a language. Parsing is the process of analyzing the syntax of a sentence.
8. Semantics: Semantics deals with the meaning of words, phrases, and sentences in a language. It explores how words relate to one another.
9. Named Entity Recognition (NER): NER is the task of identifying and classifying named entities in text, such as names of people, places, organizations, dates, etc.
10. Sentiment Analysis: Sentiment analysis, or opinion mining, is the process of determining the sentiment or emotional tone of a piece of text, often categorized as positive, negative, or neutral.
11. Machine Translation: Machine translation is the automated translation of text from one language to another, often performed by machine translation models or systems.
12. Language Model: A language model is a statistical or machine learning model that predicts the probability of a word or sequence of words given the context of a sentence.
13. Word Embeddings: Word embeddings are vector representations of words that capture their semantic meaning. Examples include Word2Vec, GloVe, and FastText.
14. Transformer Model: The Transformer is a deep learning architecture that has revolutionized NLP and is the foundation of many modern NLP models, such as BERT, GPT, and T5.
15. Pre-trained Models: Pre-trained models are NLP models that have been trained on large corpora and are fine-tuned for specific NLP tasks. They are often used for transfer learning.
16. Attention Mechanism: Attention mechanisms, such as self-attention, are key components of Transformer models and help the model focus on relevant parts of the input sequence.
17. N-grams: N-grams are contiguous sequences of N items (usually words) in a text, used for various NLP tasks, including language modeling and text generation.
18. Bag-of-Words (BoW): BoW is a simple representation of text as a collection of word frequencies, disregarding word order. It's used for text classification and information retrieval.
19. TF-IDF (Term Frequency-Inverse Document Frequency): TF-IDF is a statistical measure used to evaluate the importance of a word in a document relative to a corpus.
20. Chatbot: A chatbot is a computer program or AI system designed to simulate human conversation, often used for customer support or information retrieval.