Enhance Your Learning with Information Retrieval Flash Cards for quick understanding
The process of obtaining relevant information from a collection of unstructured or semi-structured data.
The process of analyzing and understanding user queries to retrieve relevant documents from a database or search engine.
The process of ordering search results based on their relevance to the user's query, usually using algorithms and ranking models.
The process of creating an index, which is a data structure that allows for efficient retrieval of information based on certain criteria.
Mathematical models that define how documents are ranked and retrieved based on their relevance to a query, such as the vector space model or the probabilistic model.
The process of assessing the effectiveness and performance of information retrieval systems, often using metrics like precision, recall, and F1 score.
The process of searching for information on the World Wide Web, usually performed using search engines like Google or Bing.
The process of categorizing or labeling text documents into predefined classes or categories, often used for tasks like sentiment analysis or spam detection.
The process of automatically extracting structured information from unstructured or semi-structured text, such as extracting names or dates from news articles.
The field of study that focuses on the interaction between computers and human language, including tasks like text generation, machine translation, and sentiment analysis.
A branch of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed.
A metric used to measure the proportion of retrieved documents that are relevant to a query, calculated as the number of true positives divided by the sum of true positives and false positives.
A metric used to measure the proportion of relevant documents that are retrieved by a search or retrieval system, calculated as the number of true positives divided by the sum of true positives and false negatives.
A metric used to measure the balance between precision and recall, calculated as the harmonic mean of precision and recall.
Common words that are often removed from text during information retrieval or natural language processing tasks, as they do not carry much meaning or contribute to the relevance of a document.
A numerical statistic that reflects the importance of a term in a document within a collection or corpus, often used for relevance ranking in information retrieval.
A retrieval model that uses Boolean operators (AND, OR, NOT) to combine search terms and retrieve documents that match the specified criteria.
A retrieval model that represents documents and queries as vectors in a high-dimensional space, allowing for similarity calculations and ranking based on cosine similarity.
A retrieval model that uses probabilistic methods to estimate the relevance of documents to a query, often based on the probability of generating the query from the document or the probability of relevance given the query.
A technique in information retrieval that involves using feedback from the user to improve the relevance of search results, often by re-ranking or refining the query.
An algorithm used by Google to rank web pages in search results, based on the number and quality of links pointing to a page.
The process of identifying and filtering out unsolicited or unwanted email messages, often using machine learning techniques and text classification algorithms.
The process of identifying and classifying named entities (e.g., names of people, organizations, or locations) in text, often used for tasks like information extraction or question answering.
The process of determining the sentiment or emotional tone of a piece of text, often used to analyze customer reviews, social media posts, or public opinion.
The task of automatically translating text or speech from one language to another, often using statistical or neural machine translation models.
A software system or framework that allows users to search and retrieve information from a collection of documents or data, often used in search engines or document management systems.
A technique in information retrieval that involves adding additional terms or synonyms to a user's query to improve retrieval performance and recall.
A data structure used in information retrieval to map terms or words to the documents or web pages that contain them, allowing for efficient retrieval and indexing.
A technique in information retrieval that uses singular value decomposition to identify latent semantic relationships between terms and documents, allowing for more accurate retrieval and ranking.
The task of retrieving information written in a different language than the user's query, often using techniques like machine translation or cross-lingual information retrieval models.
Systems that provide personalized recommendations or suggestions to users, often based on their past behavior, preferences, or similarities to other users.
The process of analyzing and mining user query logs to gain insights into search behavior, improve search relevance, or identify trends and patterns.
The use of visual representations or graphical techniques to present and explore large amounts of information or data, often used to aid in information retrieval and exploration tasks.
The process of designing and creating interfaces that allow users to interact with information retrieval systems or other software applications, often focusing on usability and user experience.
A feature or functionality in search engines that provides suggestions or autocompletion for user queries, often based on popular or related search terms.
The process of modifying or refining a user's query based on feedback or suggestions, often used to improve retrieval performance or overcome ambiguous or vague queries.
The task of automatically answering questions posed by users, often using information retrieval and natural language processing techniques to find relevant answers from a collection of documents or knowledge bases.
A structured representation of knowledge or information, often used to enhance search results or provide additional context and information to users.
A search technique that aims to understand the meaning and context of user queries and documents, often using semantic analysis or natural language understanding.
A subfield of machine learning that focuses on the development and training of artificial neural networks with multiple layers, often used for tasks like image recognition, speech recognition, or natural language processing.
A computational model inspired by the structure and function of biological neural networks, often used in machine learning and artificial intelligence to solve complex problems or make predictions.
A term that refers to extremely large and complex datasets that cannot be easily managed or processed using traditional data processing techniques, often requiring specialized tools and algorithms for analysis and retrieval.
The process of discovering patterns, relationships, or insights from large datasets, often using techniques from statistics, machine learning, or database systems.
A condition where the amount of information available exceeds the capacity of an individual to process or absorb it, often leading to difficulties in finding relevant or useful information.
The process of tailoring or customizing information retrieval or search results based on the preferences, interests, or characteristics of individual users.
The task of retrieving information that is relevant to the current context or situation, often taking into account factors like location, time, or user preferences.
The task of retrieving information on mobile devices, often involving challenges like limited screen size, slower network connections, or context-awareness.
The task of retrieving and searching for multimedia content, such as images, videos, or audio files, often using techniques like content-based retrieval or image recognition.
The task of retrieving information across different modalities or types of media, such as retrieving images based on textual queries or finding related documents based on image content.
The information or input provided by users about the relevance or usefulness of search results, often used to improve retrieval performance or personalize search experiences.
The underlying goal or purpose behind a user's query, often used to infer the user's information needs and provide more relevant search results or suggestions.
The process of analyzing and interpreting user queries to understand their intent, context, or specific information needs, often involving techniques from natural language processing or machine learning.
The process of improving the visibility and ranking of a website or web page in search engine results, often involving techniques like keyword optimization, link building, or content creation.