Glossary of AI Terms
Artificial intelligence (AI) and natural language (NL) technologies are critical to the enterprise business but, for many, are difficult to assess due to their complexity and nuance. No one, however, should be excluded from such an important conversation. For this very reason, we have compiled a glossary of AI- and NL-specific terms to help simplify the conversation.
The following list of terms covers words and phrases that are essential to building and expanding your knowledge of natural language and artificial intelligence technologies. With them, you can confidently navigate your journey toward adopting and implementing natural language processing and natural language understanding solutions at your enterprise organization.
Accuracy is a scoring system in binary classification (i.e., determining if an answer or output is correct or not) and is calculated as (True Positives + True Negatives) / (True Positives + True Negatives + False Positives + False Negatives).
Want to get more about accuracy? Read this article on our Community.
Information you can leverage to support decision making.
In linguistics, an anaphora is a reference to a noun by way of a pronoun. For example, in the sentence, “While John didn’t like the appetizers, he enjoyed the entrée,” the word “he” is an anaphora.
The process of tagging language data by identifying and flagging grammatical, semantic or phonetic elements in language data.
Commonly referred to as a neural network, this system consists of a collection of nodes/units that loosely mimics the processing abilities of the human brain.
The application of machine learning, natural language processing (NLP), and other AI-guided techniques to automatically classify text in a faster, more cost-effective, and more accurate manner.
Auto-complete is a search functionality used to suggest possible queries based on the text being used to compile a search query.
Google’s technology. A large scale pretrained model that is first trained on very large amounts of unannotated data. The model is then transferred to an NLP task where it is fed another smaller task-specific dataset which is used to fine-tune the final model.
In linguistics, a cataphora is a reference placed before any instance of the noun it refers to. For example, in the sentence, “Though he enjoyed the entrée, John didn’t like the appetizers,” the word “he” is a cataphora.
Categorization is a natural language processing function that assigns a category to a document.
Want to get more about categorization? Read our blog post “How to Remove Pigeonholing from Your Classification Process“.
A category is a label assigned to a document in order to describe the content within said document.
Enables you to view all of the rule-based categories in a collection. Used to create categories, delete categories, and edit the rules that associate documents with categories. Is also called a taxonomy, and is arranged in a hierarchy.
Techniques that assign a set of predefined categories to open-ended text to be used to organize, structure, and categorize any kind of text – from documents, medical records, emails, files, within any application and across the web or social media networks.
A co-occurrence commonly refers to the presence of different elements in the same document. It is often used in business intelligence to heuristically recognize patterns and guess associations between concepts that are not naturally connected (e.g., the name of an investor often mentioned in articles about startups successfully closing funding rounds could be interpreted as the investor is particularly good at picking his or her investments.).
A mental representation (otherwise known as a mental palace) which serves an individual to acquire, code, store, recall, and decode information about the relative locations and attributes of phenomena in their environment.
The output from a generative prompt.
The combined application of different AI techniques to improve the efficiency of learning in order to broaden the level of knowledge representations and, ultimately, to solve a wider range of business problems in a more efficient manner.
Computational linguistics is an interdisciplinary field concerned with the computational modeling of natural language.
Find out more about Computational linguistics on our blog reading this post “Why you need text analytics“.
Computational semantics is the study of how to automate the construction and reasoning of meaning representations of natural language expressions.
Learn more about Computational semantics on our blog reading this post “Word Meaning and Sentence Meaning in Semantics“.
Individual containers of information — that is, documents — that can be combined to form training data or generated by Generative AI.
The process of applying advanced techniques such as machine learning, artificial intelligence, and language processing to automatically extract meaningful information from your text-based documents.
A controlled vocabulary is a curated collection of words and phrases that are relevant to an application or a specific industry. These elements can come with additional properties that indicate both how they behave in common language and what meaning they carry, in terms of topic and more.
While the value of a controlled vocabulary is similar to that of taxonomy, they differ in that the nodes in taxonomy are only labels representing a category, while the nodes in a controlled vocabulary represent the words and phrases that must be found in a text.
Used by developers to build conversational user interfaces, chatbots and virtual assistants for a variety of use cases. They offer integration into chat interfaces such as messaging platforms, social media, SMS and websites. A conversational AI platform has a developer API so third parties can extend the platform with their own customizations.
A deep learning class of neural networks with one or more layers used for image recognition and processing.
The entire set of language data to be analyzed. More specifically, a corpus is a balanced collection of documents that should be representative of the documents an NLP solution will face in production, both in terms of content as well as distribution of topics and concepts.
A model built specifically for an organization or an industry – for example Insurance.
The process of uncovering data insights and getting those insights to the users who need them, when they need them.
Data Drift occurs when the distribution of the input data changes over time; this is also known as covariate shift.
Data extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured.
The process of obtaining disparate data from multiple sources, restucturing it, and importing it into a common format or repository to make it easy to utilize.
A technique through which data is marked to make objects recognizable by machines. Information is added to various data types (text, audio, image and video) to create metadata used to train AI models.
The lack of data that could possibly satisfy the need of the system to increase the accuracy of predictive analytics.
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. In other words, deep learning models can learn to classify concepts from images, text or sound.
In this blog post “Word Meaning and Sentence Meaning in Semantics” you can find more about Deep Learning.
“Did You Mean” is an NLP function used in search applications to identify typos in a query or suggest similar queries that could produce results in the search database being used.
Disambiguation, or word-sense disambiguation, is the process of removing confusion around terms that express more than one meaning and can lead to different interpretations of the same string of text.
Want to learn more? Read our blog post “Disambiguation: The Cornerstone of NLU“.
The experience and expertise your organization has acquired over time.
A model that includes data typically outside centralized cloud data centers and closer to local devices or individuals — for example, wearables and Internet of Things (IoT) sensors or actuators.
A set of data structures in a large language model (LLM) of a body of content where a high-dimensional vector represents words. This is done so data is more efficiently processed regarding meaning, translation and generation of new content.
AI to analyze the emotional state of a user (via computer vision, audio/voice input, sensors and/or software logic). It can initiate responses by performing specific, personalized actions to fit the mood of the customer.
An entity is any noun, word or phrase in a document that refers to a concept, person, object, abstract or otherwise (e.g., car, Microsoft, New York City). Measurable elements are also included in this group (e.g., 200 pounds, 14 fl. oz.)
An acronym initially used in business and government pertaining to enterprises’ societal impact and accountability; reporting in this area is governed by a set of binding and voluntary regulatory reporting.
Entity extraction is an NLP function that serves to identify relevant entities in a document.
An AI approach where the performance of its algorithms can be trusted and easily understood by humans. Unlike black-box AI, the approach arrives at a decision and the logic can be seen behind its reasoning and results.
Mutiple words that describe the main ideas and essence of text in documents.
An F-score is the harmonic mean of a system’s precision and recall values. It can be calculated by the following formula: 2 x [(Precision x Recall) / (Precision + Recall)]. Criticism around the use of F-score values to determine the quality of a predictive system is based on the fact that a moderately high F-score can be the result of an imbalance between precision and recall and, therefore, not tell the whole story. On the other hand, systems at a high level of accuracy struggle to improve precision or recall without negatively impacting the other.
Critical (risk) applications that value information retrieval more than accuracy (i.e., producing a large number of false positives but virtually guaranteeing that all the true positives are found) can adopt a different scoring system called F2 measure, where recall is weighed more heavily. The opposite (precision is weighed more heavily) is achieved by using the F0.5 measure.
Read this article on our Community to learn more about F-score.
In contrast to traditional models, which require many training examples, few-shot learning uses only a small number of training examples to generalize and produce worthwhile output.
A model focused on a specific context or category of information, such as a topic, industry or problem set
Improving an existing, pretrained model through additional training with new, context- or task-specific data.
A baseline model used for a solution set, typically pretrained on large amounts of data using self-supervised learning. Applications or other models are used on top of foundational models — or in fine-tuned contextualized versions. Examples include BERT, GPT-n, Llama, DALL-E, etc.
A model that does not specifically focus on use cases or information.
AI techniques that learn from representations of data and model artifacts to generate new artifacts.
Using LLM functionality to take text prompt inputs like long form chats, emails, reports, contracts, policies, etc and distilling them down to their core content, generating summaries from the text prompts for quick comprehension. Thus using pre-trained language models and context understanding to produce concise, accurate and relevant summaries.
The ability of generative applications to map the factual information contained in a generative output or completion. It links generative applications to available factual sources — for example, documents or knowledge bases — as a direct citation, or it searches for new links.
Made up data presented as fact in generated text that is plausible but are, in fact, inaccurate or incorrect. These fabrications can also include fabricated references or sources.
Made up data that include fabricated, inaccurate or misaligned references or sources that are presented as fact in generated text.
Hybrid AI is any artificial intelligence technology that combines multiple AI methodologies. In NLP, this often means that a workflow will leverage both symbolic and machine learning techniques.
Want to learn more about hybrd AI? Read this blog post “What Is Hybrid Natural Language Understanding?“.
These are adjustable model parameters that are tuned in order to obtain optimal performance of the model.
A component of a [expert] system that applies logical rules to the knowledge base to deduce new or additional information.
An insight engine, also called cognitive search or enterprise knowledge discovery. It applies relevancy methods to describe, discover, organize and analyze data. It combines search with AI capabilities to provide information for users and data for machines. The goal of an insight engine is to provide timely data that delivers actionable intelligence.
This is the ability to automically read and convert unstructured and semi-structured data, identify usable data and extract it, then leveraged it via automated processes. IDP is often an enabling technology for Robotic Process Automation (RPA) tasks.
A method for helping computers replicate human-like knowledge. Knowledge engineers build logic into knowledge-based systems by acquiring, modeling and integrating general or domain-specific knowledge into a model.
A knowledge graph is a graph of concepts whose value resides in its ability to meaningfully represent a portion of reality, specialized or otherwise. Every concept is linked to at least one other concept, and the quality of this connection can belong to different classes (see: taxonomies).
The interpretation of every concept is represented by its links. Consequently, every node is the concept it represents only based on its position in the graph (e.g., the concept of an apple, the fruit, is a node whose parents are “apple tree”, “fruit”, etc.). Advanced knowledge graphs can have many properties attached to a node including the words used in language to represent a concept (e.g., “apple” for the concept of an apple), if it carries a particular sentiment in a culture (“bad”, “beautiful”) and how it behaves in a sentence.
Learn more about knowledge graph reafding this blog post “Knowledge Graph: The Brains Behind Symbolic AI” on our blog.
Machine-readable data structures representing knowledge of the physical and digital worlds and their relationships. Knowledge graphs adhere to the graph model — a network of nodes and links.
A process of creating a computer interpretable model of knowledge or standards about a language, domain, or process(es). It is expressed in a data structure that enables the knowledge to be stored in a database and be interpreted by software.
Knowledge-based systems (KBs) are a form of artificial intelligence (AI) designed to capture the knowledge of human experts to support decision-making and problem-solving.
see Data Labelling.
The workflows and practices that support the training, creation, testing, production deployment and ongoing curation of language models and natural language solutions.
Language data is data made up of words; it is a form of unstrcutured data. This is qualitative data and also known as text data, but simply it refers to the written and spoken words in language.
A supervised learning algorithm that uses ensemble learning method for regression. Ensemble learning method is a technique that combines predictions from multiple machine learning algorithms to make a more accurate prediction than a single model.
The base form of a word representing all its inflected forms.
Knowledge of all of the possible meanings of words, in their proper context; is fundamental for processing text content with high precision.
Linked data is an expression that informs whether a recognizable store of knowledge is connected to another one. This is typically used as a standard reference. For instance, a knowledge graph in which every concept/node is linked to its respective page on Wikipedia.
Machine learning is the study of computer algorithms that can improve automatically through experience and the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to do so. In NLP, ML-based solutions can quickly cover the entire scope of a problem (or, at least of a corpus used as sample data), but are demanding in terms of the work required to achieve production-grade accuracy.
Want to get more about machine learning? Read this post “What Is Machine Learning? A Definition” on our blog.
Foundational instructions on how to train the way in which the model should behave.
Data that describes or provides information about other data.
A machine learning model is the artifact produced after an ML algorithm has processed the sample data it was fed during the training phase. The model is then used by the algorithm in production to analyze text (in the case of NLP) and return information and/or predictions.
Model drift is the decay of models’ predictive power as a result of the changes in real world environments. It is caused due to a variety of reasons including changes in the digital environment and ensuing changes in relationship between variables. An example is a model that detects spam based on email content and then the content used in spam was changed.
These are parameters in the model that are determined by using the training data. They are the fitted/configured variables internal to the model whose value can be estimated from data. They are required by the model when making predictions. Their values define the capability and fit of the model.
Breaking a problem with many known solutions down into its most basic elements or forms, in order to more completely understand them. Morphological analysis is used in general problem solving, linguistics and biology.
Language models that are trained on and can understand multiple data types, such as words, images, audio and other formats, resulting in increased effectiveness in a wider range of tasks
An approach that configures a prompt representing a variable — that can be changed — to allow repetitive prompts where only the variable changes.
A subfield of artificial intelligence and linguistics, natural language processing is focused on the interactions between computers and human language. More specifically, it focuses on the ability of computers to read and analyze large volumes of unstructured language data (e.g., text).
Read our blog post “6 Real-World Examples of Natural Language Processing” to learn more about Natural Language Processing (NLP).
A subset of natural language processing, natural language understanding is focused on the actual computer comprehension of processed and analyzed unstructured language data. This is enabled via semantics.
Learn more about Natural Language Understanding (NLU) reading our blog post “What Is Natural Language Understanding?”.
Solutions that automatically convert structured data, such as that found in a database, an application or a live feed, into a text-based narrative. This makes the data easier for users to access by reading or listening, and therefore to comprehend.
A natural language input that only includes terms and phrases as they occur in spoken language (i.e. without non-language characters).
A subfield of linguistics, computer science and artificial intelligence (AI) dealing with Natural Language Processing (NLP), Natural Language Undestanding (NLU), and Natural Language Generation (NLG).
An ontology is similar to a taxonomy, but it enhances its simple tree-like classification structure by adding properties to each node/element and connections between nodes that can extend to other branches. These properties are not standard, nor are they limited to a predefined set. Therefore, they must be agreed upon by the classifier and the user.
Read our blog post “Understanding Ontology and How It Adds Value to NLU” to learn more about the ontologies.
A set of numerical weights representing neural connections or other aspects in an AI model with values that are determined by training. Large language models (LLMs) can have billions of parameters.
Identifying the single elements that constitute a text, then assigning them their logical and grammatical value.
A Part-of-Speech (POS) tagger is an NLP function that identifies grammatical information about the elements of a sentence. Basic POS tagging can be limited to labeling every word by grammar type, while more complex implementations can group phrases and other elements in a clause, recognize different types of clauses, build a dependency tree of a sentence, and even assign a logical function to every word (e.g., subject, predicate, temporal adjunct, etc.).
Find out more about Part-of-Speech (POS) tagger in this article on our Community.
Solution allows a translator to edit a document that has already been machine translated. Typically, this is done sentence-by-sentence using a specialized computer-assisted-translation application.
A software component or module that extends the functionality of an LLM system into a wide range of areas, including travel reservations, e-commerce, web browsing and mathematical calculations.
Procedures that can include various pruning routines, rule filtering, or even knowledge integration. All these procedures provide a kind of symbolic filter for noisy and imprecise knowledge derived by an algorithm.
A step in the data mining and data analysis process that takes raw data and transforms it into a format that can be understood and analyzed by computers. Analyzing structured data, like whole numbers, dates, currency and percentages is straigntforward. However, unstructured data, in the form of text and images must first be cleaned and formatted before analysis.
Given a set of results from a processed document, precision is the percentage value that indicates how many of those results are correct based on the expectations of a certain application. It can apply to any class of a predictive AI system such as search, categorization and entity recognition.
For example, say you have an application that is supposed to find all the dog breeds in a document. If the application analyzes a document that mentions 10 dog breeds but only returns five values (all of which are correct), the system will have performed at 100% precision. Even if half of the instances of dog breeds were missed, the ones that were returned were correct.
Want to learn more about precision? Read this article on our Community.
A model trained to accomplish a task — typically one that is relevant to multiple organizations or contexts. Also, a pretrained model can be used as a starting point to create a fine-tuned contextualized version of a model, thus applying transfer learning.
The first step in training a foundation model, usually done as an unsupervised learning phase. Once foundation models are pretrained, they have a general capability. However, foundation models need to be improved through fine-tuning to gain greater accuracy.
A phrase or individual keywords used as input for GenAI.
An approach that uses multiple prompts to refine a request made by a model.
The craft of designing and optimizing user requests to an LLM or LLM-based chatbot to get the most effective result, often achieved through significant experimentation.
A supervised machine learning algorithm that grows and combines multiple decision trees to create a “forest.” Used for both classification and regression problems in R and Python.
Given a set of results from a processed document, recall is the percentage value that indicates how many correct results have been retrieved based on the expectations of the application. It can apply to any class of a predictive AI system such as search, categorization and entity recognition.
For example, say you have an application that is supposed to find all the dog breeds in a document. If the application analyzes a document that mentions 10 dog breeds but only returns five values (all of which are correct), the system will have performed at 50% recall.
Find out more about recall on our Community reading this article.
A neural network model commonly used in natural language process and speech recognition allowing previous outputs to be used as inputs.
A machine learning (ML) training method that rewards desired behaviors or punishes undesired ones.
A ML algorithm that learns how to perform a task by receiving feedback from a human.
The identification of relationships is an advanced NLP function that presents information on how elements of a statement are related to each other. For example, “John is Mary’s father” will report that John and Mary are connected, and this datapoint will carry a link property that labels the connection as “family” or “parent-child.”
Responsible AI is a broad term that encompasses the business and ethical choices associated with how organizations adopt and deploy AI capabilities. Generally, Responsible AI looks to ensure Transparent (Can you see how an AI model works?); Explainable (Can you explain why a specific decision in an AI model was made?); Fair (Can you ensure that a specific group is not disadvantaged based on an AI model decision?); and Sustainable (Can the development and curation of AI models be done on an environmentally sustainable basis?) use of AI.
Retrieval-augmented generation (RAG) is an AI technique for improving the quality of LLM-generated responses by including trusted sources of knowledge, outside of the original training set, to improve the accuracy of the LLM’s output. Implementing RAG in an LLM-based question answering system has benefits: 1) assurance that an LLM has access to the most current, reliable facts, 2) reduce hallucinations rates, and 3) provide source attribution to increase user trust inh the output.
Return on Artificial Intelligence (AI) is an abbreviation for return on investment (ROI) on an AI-specific initiative or investment.
Considered the “Classical Approach” of machine translation it is based on linguistic information about source and target that allow words to have different meaning depending on the context.
Subject-Action-Object (SAO) is an NLP function that identifies the logical function of portions of sentences in terms of the elements that are acting as the subject of an action, the action itself, the object receiving the action (if one exists), and any adjuncts if present.
Read this article on our Community to learn more about Subject-Action-Object (SAO).
An approach to ML in which labeled data is created from the data itself. It does not rely on historical outcome data or external human supervisors that provide labels or feedback.
A form of knowledge representation, used in several natural language processing applications, where concepts are connected to each other by semantic relationship.
The use of natural language technologies to improve user search capabilities by processing the relationship and underlying intent between words by identifying concepts and entities such as people and organizations are revealed along with their attributes and relationships.
Semantics is the study of the meaning of words and sentences. It concerns the relation of linguistic forms to non-linguistic concepts and mental representations to explain how sentences are understood by the speakers of a language.
Learn more about semantics on our blog reading this post “Introduction to Semantics“.
Data that is stuctured in some way but does not obey the tablular structure of traditional databases or other conventional data tables most commonly organized in rows and columns. Attributes of the data are different even though they may be grouped together. A simple example is a form; a more advanced example is a object database where the data is represented in the form of objects that are related (e.g. automobile make relates to model relates to trim level).
Sentiment is the general disposition expressed in a text.
Read our blog post “Natural Language Processing and Sentiment Analysis” to learn more about sentiment.
Sentiment analysis is an NLP function that identifies the sentiment in text. This can be applied to anything from a business document to a social media post. Sentiment is typically measured on a linear scale (negative, neutral or positive), but advanced implementations can categorize text in terms of emotions, moods, and feelings.
Similarity is an NLP function that retrieves documents similar to a given document. It usually offers a score to indicate the closeness of each document to that used in a query. However, there are no standard ways to measure similarity. Thus, this measurement is often specific to an application versus generic or industry-wide use cases.
A common data model for knowledge organization systems such as thesauri, classification schemes, subject heading systems, and taxonomies.
A focused collection of information or training data used to train an AI. Specialized corpora focuses on an industry — for example, banking, Insurance or health — or on a specific business or use case, such as legal documents.
The process of analyzing recordings or live calls with speech recognition software to find useful information and provide quality assurance. Speech analytics software identifies words and analyzes audio patterns to detect emotions and stress in a speaker’s voice.
Speech recognition or automatic speech recognition (ASR), computer speech recognition, or speech-to-text, enables a software program to process human speech into a written/text format.
Structured data is the data which conforms to a specific data model, has a well-defined structure, follows a consistent order and can be easily accessed and used by a person or a computer program. Structured data are usually stored in rigid schemas such as databases.
An ML algorithm in which the computer is trained using labeled data or ML models trained through examples to guide learning.
Add to Symboilic Methodology parthethetically so it looks like this: “Symbolic Methodology (Symbolic AI)”
A symbolic methodology is an approach to developing AI systems for NLP based on a deterministic, conditional approach. In other words, a symbolic approach designs a system using very specific, narrow instructions that guarantee the recognition of a linguistic pattern. Rule-based solutions tend to have a high degree of precision, though they may require more work than ML-based solutions to cover the entire scope of a problem, depending on the application.
Want to learn more about symbolic methodology? Read our blog post “The Case for Symbolic AI in NLP Models“.
The arrangement of words and phrases in a specific order to create meaning in language. If you change the position of one word, it is possible to change the context and meaning.
See Parts-of-Speech Tagging (aka POS Tagging).
A taxonomy is a predetermined group of classes of a subset of knowledge (e.g., animals, drugs, etc.). It includes dependencies between elements in a “part of” or “type of” relationship, giving itself a multi-level, tree-like structure made of branches (the final node or element of every branch is known as a leaf). This creates order and hierarchy among knowledge subsets.
Companies use taxonomies to more concisely organize their documents which, in turn, enables internal or external users to more easily search for and locate the documents they need. They can be specific to a single company or become de-facto languages shared by companies across specific industries.
Find out more about taxonomy reading our blog post “What Are Taxonomies and How Should You Use Them?“.
A parameter that controls the degree of randomness or unpredictability of the LLM output. A higher value means greater deviation from the input; a lower value means the output is more deterministic.
A test set is a collection of sample documents representative of the challenges and types of content an ML solution will face once in production. A test set is used to measure the accuracy of an ML system after it has gone through a round of training.
Techniques used to process large volumes of unstructured text (or text that does not have a predefined, structured format) to derive insights, patterns, and understanding; the process can include determining and classifying the subjects of texts, summarizing texts, extracting key entities from texts, and identifying the tone or sentiment of texts.
A range of techniques that automatically produce short textual summaries representing longer or multiple texts. The principal purpose of this technology is to reduce employee time and effort required to acquire insight from content, either by signaling the value of reading the source(s), or by delivering value directly in the form of the summary.
Language or terminological resource “dictionary” describing relationships between lexical words and phrases in a formalized form of natural language(s), enabling the use of descriptions and relationships in text processing.
A unit of content corresponding to a subset of a word. Tokens are processed internally by LLMs and can also be used as metrics for usage and billing.
The individual words used to compose a sentence.
The collection of data used to train an AI model.
A training set is the pre-tagged sample data fed to an ML algorithm for it to learn about a problem, find patterns and, ultimately, produce a model that can recognize those same patterns in future analyses.
Read this article on our Community to learn about training set.
A technique in which a pretrained model is used as a starting point for a new ML task.
Treemaps display large amounts of hierarchically structured (tree-structured) data. The space in the visualization is split up into rectangles that are sized and ordered by a quantitative variable. The levels in the hierarchy of the treemap are visualized as rectangles containing other rectangles.
An advanced extraction technique which identifies three items (subject, predicate and object) that can be used to store information.
An AI model that can be easily configured for specific requirements. For example, by industry such as healthcare, oil and gas, departmental accounting or human resources.
The procedure of re-training a pre-trained language model using your own custom data. The weights of the original model are updated to account for the characteristics of the domain data and the task you are interested modeling. The customization generates the most accurate outcomes and best insights.
Unstructured data do not conform to a data model and have no rigid structure. Lacking rigid constructs, unstructured data are often more representative of “real world” business information (examples – Web pages, images, videos, documents, audio).
A method that uses a portion of a document as metacontext or metacontent