Enhance Your Understanding with Natural Language Processing Concept Cards for quick learning
A field of study that focuses on the interaction between computers and human language, enabling computers to understand, interpret, and generate human language.
The process of breaking text into individual words or tokens, often the first step in NLP tasks.
Common words (e.g., 'the', 'is', 'and') that are often removed from text during preprocessing as they do not carry significant meaning.
A technique used to reduce words to their base or root form, often by removing suffixes or prefixes.
A process of reducing words to their base or dictionary form, considering the word's meaning and context.
Assigning grammatical tags (e.g., noun, verb, adjective) to words in a sentence, aiding in syntactic analysis.
Identifying and classifying named entities (e.g., person names, locations, organizations) in text.
Determining the sentiment or emotion expressed in text, often categorized as positive, negative, or neutral.
Mapping words to dense vector representations, capturing semantic relationships and contextual information.
Statistical models that predict the probability of a sequence of words, enabling tasks like speech recognition and machine translation.
Automatically translating text from one language to another using computational methods.
Assigning predefined categories or labels to text documents based on their content.
Extracting structured information from unstructured text, such as identifying entities, relationships, and attributes.
Automatically generating answers to questions posed in natural language, often using large-scale knowledge bases.
Computer programs designed to simulate human conversation, often used for customer support or information retrieval.
Converting spoken language into written text, enabling voice commands and transcription services.
Generating concise summaries of longer text documents, capturing the main points and key information.
A statistical technique for discovering abstract topics or themes in a collection of documents.
Analyzing the grammatical structure of a sentence by determining the relationships between words.
Identifying expressions that refer to the same entity in a text, resolving pronouns and noun phrases.
Identifying the roles played by words or phrases in a sentence, such as agent, patient, or location.
Graphical representations of the syntactic structure of a sentence, showing the relationships between words.
Patterns used to match and manipulate text, often used for tasks like search, extraction, and validation.
Creating new features or representations from raw data to improve the performance of machine learning models.
Assessing the performance of a machine learning model using various metrics and techniques.
A technique for estimating the performance of a model by splitting the data into multiple subsets for training and testing.
Metrics used to evaluate the performance of binary classification models, measuring the trade-off between false positives and false negatives.
A metric that combines precision and recall into a single value, providing a balanced measure of model performance.
Examining and mitigating biases in NLP models to ensure fairness and avoid discrimination.
Addressing ethical issues related to NLP, such as privacy, data protection, and responsible AI practices.
Cleaning, transforming, and preparing raw data for analysis, often involving tasks like tokenization and normalization.
Removing noise, errors, or irrelevant information from the data, improving its quality and reliability.
Techniques for generating additional training data by applying transformations or introducing variations to existing data.
The process of fitting a machine learning model to the training data, learning the underlying patterns and relationships.
Optimizing the hyperparameters or configuration of a machine learning model to improve its performance.
Automatically searching for the best hyperparameter values to maximize the performance of a model.
Phenomena where a machine learning model either learns the training data too well or fails to capture the underlying patterns.
Techniques that combine multiple models to improve prediction accuracy and robustness.
A class of machine learning models inspired by the structure and function of the human brain, capable of learning complex patterns.
Neural networks designed to process sequential data, capturing dependencies and patterns over time.
Neural networks commonly used for image and text processing, leveraging convolutional layers to extract local features.
State-of-the-art models for various NLP tasks, based on self-attention mechanisms and parallel processing.
A mechanism that allows neural networks to focus on specific parts of the input, improving performance and interpretability.
Using knowledge or representations learned from one task to improve performance on a different but related task.
Quantitative measures used to assess the performance of machine learning models, such as accuracy, precision, and recall.
Analyzing and understanding the errors made by a machine learning model, identifying patterns and areas for improvement.
The process of deploying a trained model into a production environment, making it available for real-world use.