NLP (Natural Language Processing) Definitions

Here’s a list of common keywords associated with Natural Language Processing (NLP) and Artificial Intelligence (AI):

Artificial Intelligence (AI): The field of computer science dedicated to creating systems that can perform tasks requiring human intelligence, such as learning, reasoning, problem-solving, and understanding natural language.

Natural Language Processing (NLP): A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language using computational techniques and linguistic knowledge.

Machine Learning (ML): A subset of AI that involves developing algorithms that can learn patterns from data without being explicitly programmed.

Deep Learning: A subfield of ML that focuses on artificial neural networks with many layers, enabling computers to learn complex patterns and representations from large datasets.

Neural Network: A computational model inspired by the human brain’s structure and function, consisting of interconnected nodes or neurons.

Tokenization: The process of breaking text into individual words or tokens, which is an essential step in many NLP tasks.

Stemming: The process of reducing words to their root or base form by removing inflections and derivational affixes.

Lemmatization: The process of converting words to their base or dictionary form, taking into account the word’s context and part of speech.

Part-of-Speech (POS) Tagging: The process of assigning grammatical categories, such as nouns, verbs, and adjectives, to words in a text.

Named Entity Recognition (NER): The task of identifying and classifying entities, such as people, organizations, and locations, within a text.

Sentiment Analysis: The process of determining the sentiment or emotional tone behind a piece of text, such as positive, negative, or neutral.

Text Classification: The task of assigning predefined categories or labels to a given text based on its content.

Machine Translation: The process of automatically translating text from one language to another using NLP techniques.

Chatbot: An AI program that can understand and respond to human language, typically used for customer support or information retrieval.

Information Extraction: The process of automatically extracting structured information, such as facts or relationships, from unstructured text.

Topic Modeling: An unsupervised ML technique used to discover the main topics or themes within a collection of documents.

Word Embedding: A representation of words as high-dimensional vectors, capturing semantic and syntactic relationships between words.

Sequence-to-Sequence (Seq2Seq) Model: A neural network architecture used for tasks that involve mapping input sequences to output sequences, such as machine translation and text summarization.

Attention Mechanism: A technique used in neural networks to weigh the importance of input elements when generating output, often used in Seq2Seq models to improve performance.

Transformer Architecture: A neural network architecture introduced by Vaswani et al. (2017) that relies on self-attention mechanisms, widely used in state-of-the-art NLP models like BERT and GPT.

BERT (Bidirectional Encoder Representations from Transformers): A pre-trained language model developed by Google that can be fine-tuned for various NLP tasks, such as sentiment analysis and text classification.

GPT (Generative Pre-trained Transformer): A series of autoregressive language models developed by OpenAI, known for their ability to generate coherent and contextually relevant text.

These definitions cover some of the most common keywords in NLP and AI. Familiarizing yourself with these terms will help you better understand the field and its related concepts.

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