Course Glossaries

Digital Humanities (DH): An interdisciplinary field that combines the use of digital tools, technology, and humanities scholarship to explore, analyze, and preserve human culture, history, and society.

Text Analysis: The use of computational tools and techniques to examine large corpora of text to identify patterns, trends, and themes that may not be immediately apparent through traditional analysis.

Data Visualization: The representation of data in a visual format, such as graphs or maps, to help uncover patterns, relationships, and trends in the data, making complex information more accessible and understandable.

Digital Archives: Repositories of digital copies of cultural artifacts, manuscripts, and other historical documents that have been digitized for preservation and public access.

Geospatial Analysis: The study of spatial relationships and patterns using geographic information systems (GIS) to map and analyze data related to geography and human activities.

Cultural Heritage Preservation: The use of digital technologies to digitize, catalog, and conserve cultural artifacts and historical sites, ensuring their long-term accessibility and conservation.

Interdisciplinary Research: Research that integrates methods, theories, and tools from multiple academic disciplines to address complex questions that cannot be fully understood within a single field.

Crowdsourcing: The practice of engaging the public in data collection, analysis, or enhancement, often used in DH projects to gather and improve information about cultural objects.

Machine Learning: A branch of artificial intelligence that enables computers to learn from and make predictions or decisions based on data, increasingly used in DH for text analysis and data processing.

Natural Language Processing (NLP): A field of computer science that focuses on the interaction between computers and human language, often used in DH for tasks such as text analysis, translation, and sentiment analysis.

3D Scanning: The process of capturing the shape and appearance of real-world objects in three dimensions using specialized equipment, often used in DH to create digital replicas of cultural artifacts.

Virtual Reality (VR): An immersive digital environment that simulates real or imagined worlds, used in DH to allow users to interact with and explore digital representations of historical sites or artifacts.

Open Access: The principle of providing free and unrestricted access to research outputs, including digital cultural heritage materials, to promote knowledge sharing and democratization.

Metadata: Descriptive information about a digital object, such as title, author, and date, used to facilitate discovery, retrieval, and management of digital resources.

Digital Literacy: The ability to effectively use digital tools and technologies for research, communication, and problem-solving, an essential skill in DH.

Artificial Intelligence (AI): The simulation of human intelligence by machines, particularly computer systems, which is increasingly being integrated into DH for tasks such as automated analysis and pattern recognition.

Interoperability: The ability of different systems, devices, or software to work together and share data, crucial for DH projects that involve collaboration across institutions and platforms.

Data Curation: The process of organizing, managing, and preserving data throughout its lifecycle to ensure its long-term accessibility and usability, important in DH for maintaining digital collections.

Ethical Considerations in DH: The examination of moral issues related to data privacy, consent, representation, and bias in digital projects, ensuring that DH practices are fair and respectful.

Digital Divide: The gap between those who have access to modern information and communication technology and those who do not, an important consideration in DH efforts to make knowledge accessible to all.

Multimodal Research: An approach that combines different forms of data (text, image, audio, video) to provide a more comprehensive analysis of cultural artifacts and historical events.

Algorithmic Bias: The presence of systematic errors in computer algorithms that lead to unfair outcomes, a critical issue in DH where data-driven decisions must be scrutinized for fairness.

Data-Driven Research: A research approach that relies heavily on the analysis of large datasets to identify trends, patterns, and relationships, increasingly common in DH.

Sentiment Analysis: The use of computational tools to identify and extract subjective information, such as opinions or emotions, from text, often used in DH to analyze public discourse.

Digital Storytelling: The practice of using digital tools to tell stories, often incorporating multimedia elements, to engage audiences with historical and cultural content.

TEI (Text Encoding Initiative): A standard for the representation of texts in digital form, providing guidelines for encoding textual features such as structure, formatting, and annotations.

Digital Mapping: The creation of interactive maps that combine geographic data with other forms of information to explore spatial relationships, commonly used in DH for historical analysis.

Semantic Web: A concept in web development that allows data to be shared and reused across applications, enterprises, and communities, often utilized in DH to improve data integration and retrieval.

Corpus Linguistics: The study of language as expressed in corpora (bodies of text) and the analysis of linguistic patterns within these texts, a key method in DH.

Digital Pedagogy: The use of digital tools and technologies to enhance teaching and learning, particularly in the context of humanities education.

Text Encoding: The process of converting text into a digital format that preserves its structure and meaning, often using standards like TEI in DH projects.

Digital Exhibitions: Online presentations of cultural artifacts, artworks, or historical documents that allow for interactive exploration and engagement by the public.

Archival Digitization: The process of converting physical archives into digital formats to preserve them and make them accessible for research and public access.

Ontology in DH: The study of how concepts and categories in a subject area are related, often used in DH to organize and structure knowledge in digital databases.

Linguistic Annotation: The process of adding metadata to text to mark linguistic features such as parts of speech, syntax, and semantics, used in DH for detailed text analysis.

Historical GIS (Geographic Information Systems): The application of GIS technology to historical research, allowing scholars to visualize and analyze past events in spatial context.

Computational Stylistics: The use of computational methods to analyze literary style, such as authorial voice or genre characteristics, a growing field within DH.

Digital Philology: The study of texts and languages using digital tools to analyze, edit, and preserve literary works, often involving the digitization of manuscripts.

Network Analysis in DH: The use of network theory to analyze relationships and interactions within datasets, such as social networks or connections between historical figures.

Crowdsourced Transcription: The process of enlisting the public to transcribe digitized documents, often used in DH projects to enhance accessibility and data quality.

Interactive Timelines: Digital tools that allow users to explore historical events in a chronological format, often used in DH to provide context and connections between events.

Digital Manuscripts: Digitally scanned versions of historical manuscripts that have been preserved and made accessible through digital archives and libraries.

Cultural Analytics: The use of computational tools to analyze cultural data, such as visual art, music, or literature, to uncover patterns and trends in cultural production.

Public Humanities: Efforts to engage the public in humanities research and education, often facilitated by digital tools and platforms in the DH context.

Data Mining in DH: The process of discovering patterns, correlations, and trends in large datasets, used in DH to analyze vast amounts of cultural and historical data.

Visual Analytics: The combination of data analysis and visual representation techniques to explore complex datasets, commonly used in DH for exploratory analysis.

Digital Repositories: Online databases that store and provide access to digital objects such as texts, images, and multimedia, essential for the preservation and dissemination of DH research.

Machine Translation in DH: The use of automated tools to translate text from one language to another, increasingly important in multilingual DH projects.

Digital Annotation: The practice of adding notes, comments, or metadata to digital texts or images, often used in DH to enhance understanding and analysis.

Big Data in DH: The use of large and complex datasets in DH research, which require advanced computational tools for storage, analysis, and visualization.

Machine Translation (MT): The process of automatically translating text or speech from one language to another using computer algorithms.

Rule-Based Machine Translation (RBMT): An MT approach that uses linguistic rules and bilingual dictionaries to translate text, focusing on syntax, morphology, and grammar.

Statistical Machine Translation (SMT): An MT approach that uses statistical models based on bilingual text corpora to predict the probability of a translation.

Neural Machine Translation (NMT): An advanced MT approach that uses deep learning models, specifically neural networks, to translate text by analyzing large datasets and capturing context.

Example-Based Machine Translation (EBMT): An MT approach that relies on a database of previously translated examples, finding the closest matches to translate new sentences.

Hybrid Machine Translation: A combination of different MT approaches, often integrating RBMT and SMT/NMT to leverage the strengths of each method.

Bilingual Text Corpora: Large collections of text in two languages, used to train and evaluate MT systems by providing parallel examples of translations.

Parallel Corpora: A type of bilingual corpus where texts in two languages are aligned at the sentence level, facilitating the training of SMT and NMT systems.

Phrase-Based Machine Translation: A specific type of SMT that breaks down text into phrases rather than individual words, improving the fluency of translations.

Sequence-to-Sequence (Seq2Seq) Model: A deep learning model used in NMT that processes sequences of text to generate translations, maintaining the order and context of words.

Translation Model: In SMT, a model that predicts the most likely translation of a word or phrase based on bilingual text data.

Language Model: A model that assesses the fluency of the translated text by predicting the likelihood of word sequences in the target language.

Decoding Algorithm: In MT, the process that selects the best translation hypothesis based on the probabilities generated by the translation and language models.

Reordering Model: A component in SMT that predicts the correct word order in the target language, addressing differences in syntax between languages.

Neural Networks: Computational models inspired by the human brain, used in NMT to learn patterns and relationships in language data.

Attention Mechanism: A technique in NMT that allows the model to focus on specific parts of the input sentence, improving translation accuracy, especially for long sentences.

Encoder-Decoder Architecture: A framework used in NMT where the encoder processes the input text and the decoder generates the translation, often using an attention mechanism.

BLEU Score (Bilingual Evaluation Understudy): A metric for evaluating the quality of machine-generated translations by comparing them to one or more reference translations.

Pre-trained Models: In NMT, models that have been trained on large datasets and can be fine-tuned for specific tasks or languages, speeding up the development process.

Transfer Learning: The practice of applying knowledge gained from one task (e.g., translating English to French) to another related task (e.g., translating English to Spanish), commonly used in NMT.

Back-Translation: A method in NMT training where target language data is translated back into the source language to create additional training data, improving translation quality.

Subword Units: Smaller language components, such as prefixes or suffixes, used in NMT to handle rare or compound words more effectively.

Tokenization: The process of breaking down text into smaller units, such as words or subwords, to facilitate processing in MT systems.

Alignment: The process of matching corresponding words or phrases between the source and target languages in a parallel corpus, crucial for training SMT and NMT systems.

Word Embeddings: Dense vector representations of words used in NMT to capture semantic meanings and relationships between words in different languages.

Domain Adaptation: The process of fine-tuning an MT system to perform better in a specific domain, such as legal or medical translation.

Cross-Lingual Transfer: The ability of an MT system to apply knowledge from one language pair to another, enhancing translation quality across multiple languages.

Multilingual Translation: An NMT approach that handles multiple languages simultaneously, using a shared model that can translate between any pair of supported languages.

Low-Resource Languages: Languages that have limited digital resources, such as corpora or dictionaries, posing challenges for MT development.

Out-of-Vocabulary (OOV) Words: Words that are not present in the training data of an MT system, often leading to translation errors.

Post-Editing: The process of manually correcting errors in machine-generated translations to improve accuracy and fluency.

Syntactic Parsing: The process of analyzing the grammatical structure of sentences, used in RBMT to generate accurate translations.

Morphological Analysis: The study of the structure of words and their components, such as roots and affixes, used in RBMT to handle inflected languages.

Lexical Disambiguation: The process of determining the correct meaning of a word that has multiple possible interpretations, crucial in MT for accurate translations.

Semantic Role Labeling: Identifying the roles played by words in a sentence, such as agent or object, to improve the accuracy of MT systems.

Language Pair: The combination of a source language and a target language in MT, such as English to Spanish.

Pivot Language: An intermediate language used in MT when direct translation between two languages is difficult due to lack of resources.

Contextual Embeddings: Word embeddings that take into account the context in which a word appears, improving translation quality in NMT.

Data Augmentation: The process of artificially increasing the size of a training dataset by creating variations of existing data, used to improve MT performance.

Beam Search: A decoding algorithm used in NMT that considers multiple translation hypotheses simultaneously to find the most probable translation.

Dropout: A regularization technique in NMT that prevents overfitting by randomly dropping units in the neural network during training.

Parallel Sentence Mining: The process of automatically finding and extracting parallel sentences from large bilingual corpora, used to improve the training of MT systems.

Translationese: A term referring to the distinct linguistic patterns that emerge in machine-generated translations, often detectable by statistical analysis.

Interactive MT: An MT approach where human translators interact with the MT system during the translation process, refining the output in real-time.

Corpus-Based MT: An approach that relies heavily on large text corpora for training MT systems, typical in SMT and NMT.

Phrase Table: In SMT, a table that lists possible translations for phrases in the source language along with their probabilities.

Cross-Entropy Loss: A loss function used in NMT training to measure the difference between the predicted translation and the actual translation.

Neural Language Model: A type of language model used in NMT that predicts the next word in a sentence based on the context of previous words.

Knowledge Distillation: A technique in NMT where a smaller, simpler model is trained to replicate the behavior of a larger, more complex model, improving efficiency.

Transfer-Based MT: An MT approach that transfers linguistic structures from the source language to the target language, relying on syntactic and semantic transfer rules.

Language for Specific Purposes (LSP): A specialized branch of applied linguistics focusing on the linguistic needs of particular professional or academic groups, differing from general language use.

Specialized Vocabulary: Terms and phrases used within a specific field, such as medicine or law, that have precise meanings unique to that domain.

Academic Writing: A style of writing characterized by complex sentence structures, formal tone, and the use of specialized vocabulary relevant to a particular academic discipline.

Legal Language: The formal, precise language used in legal documents, often characterized by complex sentence structures and archaic vocabulary.

Business Communication: The use of clear, concise language in business contexts, often involving direct sentence structures and conditional phrases for proposals and negotiations.

Technical Writing: Writing that conveys technical information, often characterized by sequential structures and the use of specialized vocabulary.

Journalistic Writing: A style of writing used in news media, typically characterized by the inverted pyramid structure and the use of active voice for immediacy.

Medical Communication: The specialized use of language in medical contexts, often involving Latin or Greek-derived terminology and passive sentence structures.

Scientific Research Papers: Formal academic papers that present scientific research, characterized by precise terminology, structured presentation, and a focus on clarity and objectivity.

Purpose-Driven Communication: Communication tailored to specific objectives within a particular field, focusing on clarity, precision, and efficiency.

Clarity and Precision: The use of language that is clear and precise to avoid ambiguity, particularly important in fields like law and science.

Conciseness: The practice of conveying information efficiently without superfluous details, often employed in professional communication.

Formality and Professionalism: A tone of language that conveys seriousness and respect, often through the use of formal vocabulary and structures.

Objective Tone: A neutral, factual style of writing that minimizes emotional language, common in academic and professional contexts.

Jargon: Specialized language used by professionals within a specific field, often difficult for outsiders to understand.

Target Audience Consideration: Tailoring language to the knowledge level and interests of the intended audience, whether specialized or general.

Structured and Organized: The practice of organizing communication in a logical and coherent manner, with a clear beginning, middle, and end.

Call to Action: A directive in communication that encourages the audience to take a specific action, common in marketing and advocacy.

Persuasive Elements: The use of rhetorical devices and compelling arguments to influence the audience's beliefs or actions.

Cultural Sensitivity: The practice of crafting language with an awareness of cultural norms and expectations, particularly in international contexts.

Audience and Context Awareness: The focus on the specific needs and background of the audience in LSP, influencing vocabulary and communication style.

Pragmatic and Functional Approach: An emphasis on the practical use of language to achieve effective communication within a specific domain.

Interdisciplinary Nature: The intersection of LSP with various disciplines, requiring an understanding of both linguistic principles and the specific knowledge domain.

Cultural and Contextual Sensitivity: Awareness of the cultural and situational contexts in which language is used, acknowledging unique communicative conventions.

Dynamic and Evolving Language: The ongoing adaptation of LSP to new developments, terminologies, and communication needs within specific sectors.

Term (Terminology): A word or phrase used in a specific context within a specialized field to denote a precise concept or object.

General Vocabulary: Words that are known and used by all speakers of a language, forming the basis of everyday communication.

Technical Vocabulary (Jargon): Specialized terms and expressions used primarily within specific professional fields, conveying complex ideas efficiently.

Sublanguage or Lingo: The specialized language used by a particular group or community, encompassing jargon and specific expressions.

Simple Terms: Terms consisting of a single lexical unit (word), such as "atom" in physics or "aorta" in anatomy.

Compound Terms: Terms formed by combining two or more words to describe a new concept or specific aspect, such as "hard drive" or "power of attorney."

Complex Terms (Phrasal Terms): Phrases consisting of multiple words that together form a single concept, such as "habeas corpus" or "natural selection."

Abbreviations and Acronyms: Shortened forms of terms, such as "DNA" (Deoxyribonucleic Acid) or "laser" (Light Amplification by Stimulated Emission of Radiation).

Neologisms: Newly coined terms created to describe new inventions, concepts, or phenomena.

Borrowed Terms: Terms borrowed from other languages, often retaining their foreign spelling and pronunciation, common in fields like medicine.

Derivative Terms: Terms formed by adding prefixes and suffixes to change the meaning of a base word, such as "unemployment."

Syntactic Structure: The arrangement of words in a term or phrase that conveys precise meaning, important in fields like medical diagnostics.

Epistemology in LSP: The study of how knowledge is structured and represented within specific fields through language.

Terminological Standardization: The process of creating and maintaining a consistent set of terms within a specific field to ensure clarity and accuracy.

Terminological Variation: The differences in terminology use across different regions, cultures, or subfields within a discipline.

Multilingual Terminology: The practice of developing and managing terms across multiple languages, important in international and cross-cultural communication.

Ontological Approach to Terminology: The organization of terminological data based on the relationships between concepts, aiding in the understanding and retrieval of specialized knowledge.

Terminology Translation: The process of accurately translating terms from one language to another, ensuring that the meaning and nuance are preserved.

Lexical Semantics in LSP: The study of word meanings and their relationships within the vocabulary of a specific field.

Collocations in LSP: The habitual juxtaposition of a particular word with another word or words with a frequency greater than chance, important in understanding specialized language patterns.

Terminological Equivalence: The degree to which a term in one language has an equivalent term in another language with the same meaning.

Corpus-Based Terminology: The use of large text corpora to analyze and develop terminology within a specific field.

Terminological Databases: Digital repositories that store and manage specialized terms, aiding in retrieval and consistency in LSP.

Semantic Networks in LSP: The mapping of relationships between terms and concepts within a specific field, used to structure and navigate specialized knowledge.

Digital Humanities and LSP: The application of digital tools and methods to the study and management of language for specific purposes, enhancing research and communication in specialized fields.

Ontology: A structured framework that organizes information by representing knowledge as a set of concepts within a domain and the relationships between those concepts. It provides a shared vocabulary for a domain and defines the meaning of terms and their relationships.

Domain: The specific area of knowledge or activity that an ontology aims to model. It defines the scope within which concepts, relationships, and properties described by the ontology are relevant.

Concepts: Abstract ideas or mental symbols used to categorize entities based on shared characteristics. In ontologies, concepts are represented as classes.

Classes: Formal representations of concepts within an ontology. Classes group objects or instances that share common characteristics.

Instances (Individuals): Specific examples or objects within a domain that populate an ontology. They are instances of classes and have specific properties.

Relationships: Connections between concepts, classes, or instances within an ontology, defining how these entities relate to one another.

Hierarchy: The organization of classes in an ontology into a tree-like structure where higher-level classes are more general, and lower-level classes are more specific.

Subclass (Is-a) Relationship: A hierarchical relationship where one class is a specialized version of another, more general class. For example, "Dog" is a subclass of "Animal."

Part-Whole (Meronymy) Relationship: A relationship where one class is a part of a whole class. For example, "Wheel" is a part of "Car."

Associative Relationship: A non-hierarchical connection between concepts or instances, such as "CollaboratesWith" or "DependsOn."

Syntagmatic Relationships: Relationships between words in a sequence within a syntactic structure, forming phrases, clauses, or sentences.

Paradigmatic Relationships: Relationships between words that can substitute for each other in a particular context, such as synonyms or antonyms.

Synonymy: A paradigmatic relationship where different words have similar or identical meanings in some contexts.

Antonymy: A paradigmatic relationship where words have opposite meanings.

Hyponymy: A relationship where a more specific word (hyponym) is related to a more general word (hypernym).

Hypernymy: The inverse of hyponymy, where a more general word encompasses more specific words.

Meronymy: A part-whole relationship where one word represents a part of something, such as "wheel" being a part of "car."

Holonymy: The inverse of meronymy, where one word represents the whole that encompasses the parts.

Troponymy: A relationship specific to verbs, where one verb represents a more specific way of performing the action described by another verb.

Metonymy: A figure of speech where a word is used to stand in for something it is closely related to.

Polysemy: A relationship where a single word has multiple related meanings.

Homonymy: A relationship where words sound the same but have different meanings (homophones) or are spelled the same but have different meanings or pronunciations (homographs).

Properties: Attributes or characteristics assigned to classes or instances in an ontology, defining their features or relationships.

Datatype Properties: Properties that link instances to data values, such as a person's age or a car's color.

Object Properties: Properties that link instances to other instances, such as a person working for a company.

Ontology Alignment: The process of matching concepts and relationships between different ontologies to enable interoperability.

Ontology Merging: The process of combining two or more ontologies into a single coherent ontology.

Ontology Mapping: The process of defining correspondences between the elements of different ontologies.

Ontology Reuse: The practice of using existing ontologies or parts of them in new ontology projects to save time and effort.

Ontology Evolution: The process of updating an ontology to reflect new knowledge or changes in the domain it models.

Ontology Validation: The process of checking an ontology for consistency, completeness, and correctness.

Ontology Inference: The process of deriving new information or conclusions based on the relationships and rules defined in an ontology.

Ontology Querying: The use of specialized languages or tools to retrieve information from an ontology based on specific criteria.

Reasoning: The process of applying logical rules to an ontology to infer new knowledge or check for consistency.

Semantic Web: A vision of the web where information is structured and linked in a way that allows machines to understand and process it, often using ontologies.

SPARQL: A query language for querying RDF (Resource Description Framework) data, commonly used in ontology querying.

RDF (Resource Description Framework): A framework for representing information about resources on the web, often used in conjunction with ontologies.

OWL (Web Ontology Language): A language used to define and instantiate Web ontologies, providing a way to model complex relationships and constraints.

Description Logic: A family of formal knowledge representation languages used to describe the relationships between concepts in ontologies.

Ontology Editor: Software tools used to create, modify, and manage ontologies, such as Protégé.

Ontology Repository: A database or storage system designed to store, manage, and share ontologies.

Upper Ontology: A high-level, abstract ontology that provides a general framework for more specific ontologies.

Domain Ontology: An ontology that represents knowledge specific to a particular domain, such as healthcare or finance.

Interoperability: The ability of different systems or ontologies to work together and exchange information seamlessly.

Knowledge Representation: The field of study concerned with how knowledge can be represented in a formal, structured way that computers can process.

Conceptualization: The process of defining the concepts and relationships within a domain that an ontology will model.

Ontology Engineering: The field of study and practice that involves the design, creation, and management of ontologies.

Formal Ontology: An ontology that is defined using formal languages and logic, allowing for precise definitions and automated reasoning.

Lexical Ontology: An ontology that focuses on the relationships between words and their meanings, often used in natural language processing.

Ontology Design Patterns: Reusable solutions to common ontology design problems, helping to ensure best practices and consistency.

Frame Semantics: A linguistic theory that studies how words and phrases evoke mental structures, or "frames," that help individuals understand and interpret language by relating it to their experiences.

Cognitive Semantics: An approach to understanding the relationship between language and thought, focusing on how language reflects mental structures and processes.

Frame: A conceptual structure that organizes knowledge and experiences, helping to predict linguistic arguments and understand the meaning of words in context.

Frame Elements: The roles or participants associated with a particular frame, such as the agent, patient, or instrument in an action.

Lexical Units (LUs): Words or phrases that evoke specific frames, contributing to the understanding of meaning within a particular context.

Fillmorean Case Roles: Semantic roles associated with frame elements, developed by Charles Fillmore, including roles like agent, patient, and instrument.

FrameNet: A computational lexicography project that documents the relationships between lexical units and their associated frames, providing a resource for understanding frame semantics.

Apply _heat Frame: A specific example of a frame that involves a scenario where heat is applied to food, involving roles like cook, food, and heating instrument.

Frame-to-Frame Relations: Relationships between different frames that help organize knowledge within an ontology, such as inheritance or causality.

Inheritance in Frames: A relationship where one frame inherits properties from a more general frame, allowing for the extension and adaptation of frames to new contexts.

Construction Grammar: A theory that views language as a collection of constructions, or form-meaning pairings, which range from simple words to complex syntactic structures.

Syntactic Constructions: Pairings of surface structure (syntax) and function (meaning) that are used to create sentences in a language.

Semantic Constructions: Constructions that encode both grammatical and semantic information, capturing regularities in meaning beyond syntax.

Constructional Polysemy: The phenomenon where a single construction can have multiple related meanings or functions, allowing for flexibility in language use.

Constructional Networks: Networks of interconnected constructions that capture the relationships between different linguistic patterns, including inheritance and constructional schemas.

Frame Evocation: The process by which a lexical unit activates a specific frame in the mind of the listener or reader.

Semantic Fields: Groups of words related in meaning, often organized around a central concept or frame.

Contextual Frame: A frame that is activated in a specific context, influencing the interpretation of language within that setting.

Pragmatics: The study of how context influences the interpretation of language, often interacting with frame semantics to determine meaning.

Conceptual Metaphor: A cognitive structure where one idea or conceptual domain is understood in terms of another, often involving the activation of specific frames.

Embodiment in Semantics: The idea that understanding language involves relating it to bodily experiences, which are often structured by frames.

Narrative Frames: Frames that organize the elements of a story or discourse, helping to structure the sequence of events and the roles of participants.

Script: A type of frame that represents a sequence of events or actions typically associated with a particular context, such as "going to a restaurant."

Prototype Theory: The idea that some members of a category are more central or typical than others, often influencing the structure of frames.

Cultural Frames: Frames that are shaped by cultural experiences and norms, influencing how language is used and interpreted within a community.

Frame Shifting: The process of moving from one frame to another in the course of understanding or producing language, often to achieve a different perspective or meaning.

Frame Activation: The mental process by which a particular frame is triggered by a word, phrase, or context.

Polysemy: The phenomenon where a single word or phrase has multiple meanings, often related to different frames.

Metonymy: A figure of speech where a concept is referred to by something closely associated with it, often involving a shift in frames.

Frame-Based Translation: An approach to translation that considers the frames evoked by the source language and seeks to evoke equivalent frames in the target language.

Semantic Roles: The functions that participants in a sentence have in relation to the main action or state described by the verb, such as agent, patient, or instrument.

Role Frames: Frames that focus on the relationships between participants in an action or event, organizing the roles they play.

Discourse Frames: Frames that organize larger units of language, such as conversations or texts, influencing how the content is structured and understood.

Frame Matching: The process of identifying and aligning frames across different languages or contexts to achieve coherence and understanding.

Frame Analysis: A method of analyzing language by identifying the frames that structure meaning and exploring their relationships.

Frame Mapping: The process of relating frames from different languages or contexts, often used in translation studies and cross-cultural communication.

Constructional Approach to Semantics: An approach that integrates frame semantics with the study of constructions, emphasizing the pairing of form and meaning.

Cognitive Frames: Mental structures that shape how we perceive, interpret, and respond to the world, often influencing language use.

Frame-Based Reasoning: A type of reasoning that relies on the activation of relevant frames to make inferences and understand new information.

Frame Integration: The combination of multiple frames to create a more comprehensive understanding of a complex concept or situation.

Lexical Frame: The specific frame or set of frames associated with a particular word or phrase.

Frame Structure: The internal organization of a frame, including its core elements, relationships, and possible variations.

Frame Annotation: The process of labeling text with information about the frames it evokes, often used in linguistic research and natural language processing.

Semantic Memory: The aspect of memory that stores knowledge about the meanings of words and concepts, often organized by frames.

Frame-Building: The process of constructing new frames or adapting existing ones to accommodate new information or contexts.

Interdisciplinary Frame Analysis: The application of frame semantics to different fields, such as sociology, psychology, and digital humanities, to analyze how language shapes understanding.

Frame-Based Ontologies: Ontologies that are structured around frames, organizing knowledge in a way that reflects the cognitive structures underlying language.

Frame Semantics in Digital Humanities: The application of frame semantics to the analysis and interpretation of digital texts and data, often involving the use of computational tools.

Frame Semantics and Natural Language Processing: The use of frame semantics in developing algorithms and systems for understanding and generating human language.

Ethical Considerations in Frame Analysis: The examination of how frames can influence perception and decision-making, raising ethical questions about their use in communication, media, and technology.

Terminology Studies: The systematic study of terms used within specialized domains, focusing on their identification, standardization, and management to ensure effective communication.

Term: A specialized word or phrase with a specific meaning within a particular domain or field of study.

Terminography: The compilation and management of terminological resources, such as glossaries, dictionaries, and databases.

Terminological Database: A structured collection of terminological data used to store, retrieve, and manage terms and their associated information.

Concept: A mental representation of an idea, object, or phenomenon that a term refers to within a specific domain.

Domain: A specific subject area or field of study characterized by its own terminology and specialized knowledge.

Standardization: The process of establishing and maintaining standardized terminology within a domain to ensure consistency and clarity in communication.

Ontology: A formal representation of concepts and relationships within a domain, used to support knowledge organization and information retrieval.

Glossary: A list of terms and their definitions compiled for a specific domain or subject area.

Dictionary: A comprehensive reference work containing definitions, explanations, and other information about terms, often organized alphabetically.

Linguistic Analysis: The examination of the linguistic properties of terms, including their morphological structure, semantic relationships, and syntactic usage.

Cognitive Semantics: A theoretical approach investigating how terms are processed, stored, and accessed in the human mind based on cognitive processes and mental representations.

Sociolinguistics: The study of how language is used and influenced by social and cultural factors, particularly within specialized communities of practice.

Functional Theory: Analyzes how terms fulfill specific communicative functions within specialized discourse communities, such as categorization, description, and argumentation.

Translation and Localization: The process of translating and adapting terminological resources for use in different languages and cultural contexts.

Terminology Management: The organization, storage, and retrieval of terminological data using various tools and techniques to ensure consistency and accuracy.

Term Collection: The process of gathering relevant terms from various sources, including technical documents, academic papers, and expert consultations.

Term Analysis: Examining each term to understand its meaning, usage, and context, including defining the term and identifying its relationships with other terms.

Term Standardization: Establishing standardized definitions and usage guidelines for terms to ensure consistency in communication.

Multilingual Terminology Management: Managing the translation and adaptation of terms into different languages, ensuring accurate and consistent rendering in each language.

Terminology Database Creation: Developing a structured database that includes terms, definitions, contexts, translations, and other relevant information.

Quality Control: Regularly reviewing and updating the terminological database to ensure the accuracy and relevance of the terms.

Integration with Tools: Linking terminological management with other tools, such as translation memory systems and content management systems, to streamline workflows.

Terminology Distribution: Making terminological resources accessible to users, such as translators, content creators, industry professionals, and researchers.

Term Definition: Providing clear and concise explanations of terms, often accompanied by additional descriptions or examples to aid understanding.

Contextual Information: Information provided alongside term definitions to explain how a term is used in practice, including examples and usage notes.

Dynamic Glossary: A glossary that is regularly updated to include new terms and reflect changes or advancements in the field.

Multilingual Glossary: A glossary that includes translations of terms into different languages, often used in international or multilingual contexts.

Subject-Specific Glossary: A glossary focused on a particular subject area or domain, ensuring that the terms included are relevant to that field.

Term Identification: The process of identifying and documenting terms used within a specific domain or field of study.

Controlled Vocabulary: A standardized set of terms and definitions used within a specific domain to ensure consistency in communication.

Lexical Semantics: The study of word meanings and their relationships within the vocabulary of a specific field.

Semantic Relations: Relationships between terms in a terminological database, such as synonyms, antonyms, or hierarchical relationships.

Terminological Consistency: The practice of using the same terms and definitions consistently across different documents, translations, and contexts.

Term Extraction: The process of identifying and extracting relevant terms from a body of text, often using automated tools.

Term Validation: The process of checking and confirming the accuracy and relevance of terms in a glossary or database.

Term Usage: The practical application of terms within specific contexts, including research into how terms are used in practice.

Term Translation: The process of translating terms from one language to another, ensuring that the meaning and nuance are preserved.

Terminological Glossary: A specialized resource that compiles and defines terms, typically specific to a particular field, subject area, or domain of knowledge.

Lexicography: The practice of compiling dictionaries, which often overlaps with terminography in the creation of glossaries and terminological databases.

Knowledge Representation: The field of study concerned with how knowledge can be represented in a formal, structured way that computers can process, often using ontologies.

Database Management: The practice of organizing and maintaining a terminological database, including updating terms and ensuring data integrity.

Interoperability: The ability of different systems, tools, or databases to work together and exchange information seamlessly.

Term Documentation: The process of recording detailed information about each term, including its definition, context, usage, and any relevant translations.

Translation Memory (TM): A database that stores previously translated segments, which can be reused in future translations to ensure consistency and save time.

Term Alignment: The process of aligning terms across different languages or contexts, often used in translation and localization.

Cognitive Approach: The exploration of how terms are processed, stored, and accessed in the human mind, often using cognitive theories like prototype theory.

Term Standardization Efforts: The organized efforts to create standardized terms within a domain, often involving collaboration with industry experts or standardization bodies.

Term Dissemination: The distribution of standardized terms and definitions to ensure they are used consistently by professionals within the domain.

Term Indexing: The practice of organizing terms in a way that facilitates easy retrieval, often using indexing systems or controlled vocabularies.