SDL plc was a British multinational professional services company based in Maidenhead, Berkshire, United Kingdom. SDL specialized in language translation software and services (including interpretation services). It was listed on the London Stock Exchange until it was acquired by RWS Group in November 2020. == Name == SDL is an abbreviation for "Software and Documentation Localization". == History == The company was founded by Mark Lancaster with nine employees in 1992. It opened its first overseas office in France in 1996 and was first listed on the London Stock Exchange in 1999. The company grew organically and via acquisitions. SDL acquired Polylang Multimedia in 1998, International Translation & Publishing (ITP) in 2000, Alpnet in 2001, and the machine translation (MT) assets of Transparent Language in 2001. It bought Trados, a rival translation memory (TM) developer, in 2005. In 2007, the company acquired Tridion, a content management system vendor, and PASS Engineering, developers of the Passolo software. In 2008, it bought Idiom Technologies, a global information system management business. In July 2009 SDL acquired XyEnterprise in an all-cash transaction to add XML Professional Publisher as well as Contenta content management software and LiveContent to manage and deliver XML. This unit combined with Trisoft formerly Infoshare. In December 2009, SDL acquired Fredhopper, a Dutch eCommerce onsite search and navigation, onsite targeting and targeted advertising software vendor. Later that same year, it bought Xopus, another Dutch company and the leader in online XML editing. In May 2011 SDL acquired Dutch-based Media Asset Management company, Calamares, in 2012 the campaign management and social media analytics company, Alterian, and in 2013, bemoko, a supplier of internet software for mobile devices. In January 2016, having undertaken a strategic review, SDL announced the divestment of Fredhopper and Alterian as non-complementary to its new strategy. In August 2020 RWS Group announced a proposed takeover of the company for £809 million. The transaction was completed on 4 November 2020. == Operations == SDL provided software for language translation purposes.
Grammar systems theory
Grammar systems theory is a field of theoretical computer science that studies systems of finite collections of formal grammars generating a formal language. Each grammar works on a string, a so-called sequential form that represents an environment. Grammar systems can thus be used as a formalization of decentralized or distributed systems of agents in artificial intelligence. Let A {\displaystyle \mathbb {A} } be a simple reactive agent moving on the table and trying not to fall down from the table with two reactions, t for turning and ƒ for moving forward. The set of possible behaviors of A {\displaystyle \mathbb {A} } can then be described as formal language L A = { ( f m t n f r ) + : 1 ≤ m ≤ k ; 1 ≤ n ≤ ℓ ; 1 ≤ r ≤ k } , {\displaystyle \mathbb {L_{A}} =\{(f^{m}t^{n}f^{r})^{+}:1\leq m\leq k;1\leq n\leq \ell ;1\leq r\leq k\},} where ƒ can be done maximally k times and t can be done maximally ℓ times considering the dimensions of the table. Let G A {\displaystyle \mathbb {G_{A}} } be a formal grammar which generates language L A {\displaystyle \mathbb {L_{A}} } . The behavior of A {\displaystyle \mathbb {A} } is then described by this grammar. Suppose the A {\displaystyle \mathbb {A} } has a subsumption architecture; each component of this architecture can be then represented as a formal grammar, too, and the final behavior of the agent is then described by this system of grammars. The schema on the right describes such a system of grammars which shares a common string representing an environment. The shared sequential form is sequentially rewritten by each grammar, which can represent either a component or generally an agent. If grammars communicate together and work on a shared sequential form, it is called a Cooperating Distributed (DC) grammar system. Shared sequential form is a similar concept to the blackboard approach in AI, which is inspired by an idea of experts solving some problem together while they share their proposals and ideas on a shared blackboard. Each grammar in a grammar system can also work on its own string and communicate with other grammars in a system by sending their sequential forms on request. Such a grammar system is then called a Parallel Communicating (PC) grammar system. PC and DC are inspired by distributed AI. If there is no communication between grammars, the system is close to the decentralized approaches in AI. These kinds of grammar systems are sometimes called colonies or Eco-Grammar systems, depending (besides others) on whether the environment is changing on its own (Eco-Grammar system) or not (colonies).
FrameNet
FrameNet is a group of online lexical databases based upon the theory of meaning known as Frame semantics, developed by linguist Charles J. Fillmore. The project's fundamental notion is simple: most words' meanings may be best understood in terms of a semantic frame, which is a description of a certain kind of event, connection, or item and its actors. As an illustration, the act of cooking usually requires the following: a cook, the food being cooked, a container to hold the food while it is being cooked, and a heating instrument. Within FrameNet, this act is represented by a frame named Apply_heat, and its components (Cook, Food, Container, and Heating_instrument), are referred to as frame elements (FEs). The Apply_heat frame also lists a number of words that represent it, known as lexical units (LUs), like fry, bake, boil, and broil. Other frames are simpler. For example, Placing only has an agent or cause, a theme—something that is placed—and the location where it is placed. Some frames are more complex, like Revenge, which contains more FEs (offender, injury, injured party, avenger, and punishment). As in the examples of Apply_heat and Revenge below, FrameNet's role is to define the frames and annotate sentences to demonstrate how the FEs fit syntactically around the word that elicits the frame. == Concepts == === Frames === A frame is a schematic representation of a situation involving various participants, props, and other conceptual roles. Examples of frame names are Being_born and Locative_relation. A frame in FrameNet contains a textual description of what it represents (a frame definition), associated frame elements, lexical units, example sentences, and frame-to-frame relations. === Frame elements === Frame elements (FE) provide additional information to the semantic structure of a sentence. Each frame has a number of core and non-core FEs which can be thought of as semantic roles. Core FEs are essential to the meaning of the frame while non-core FEs are generally descriptive (such as time, place, manner, etc.) For example: The only core FE of the Being_born frame is called Child; non-core FEs Time, Place, Means, etc. Core FEs of the Commerce_goods-transfer frame include the Seller, Buyer, and Goods, while non-core FEs include a Place, Purpose, etc. FrameNet includes shallow data on syntactic roles that frame elements play in the example sentences. For example, for a sentence like "She was born about AD 460", FrameNet would mark She as a noun phrase referring to the Child frame element, and "about AD 460" as a noun phrase corresponding to the Time frame element. Details of how frame elements can be realized in a sentence are important because this reveals important information about the subcategorization frames as well as possible diathesis alternations (e.g. "John broke the window" vs. "The window broke") of a verb. === Lexical units === Lexical units (LUs) are lemmas, with their part of speech, that evoke a specific frame. In other words, when an LU is identified in a sentence, that specific LU can be associated with its specific frame(s). For each frame, there may be many LUs associated to that frame, and also there may be many frames that share a specific LU; this is typically the case with LUs that have multiple word senses. Alongside the frame, each lexical unit is associated with specific frame elements by means of the annotated example sentences. For example, lexical units that evoke the Complaining frame (or more specific perspectivized versions of it, to be precise), include the verbs complain, grouse, lament, and others. === Example sentences === Frames are associated with example sentences and frame elements are marked within the sentences. Thus, the sentence She was born about AD 460 is associated with the frame Being_born, while She is marked as the frame element Child and "about AD 460" is marked as Time. From the start, the FrameNet project has been committed to looking at evidence from actual language use as found in text collections like the British National Corpus. Based on such example sentences, automatic semantic role labeling tools are able to determine frames and mark frame elements in new sentences. === Valences === FrameNet also exposes statistics on the valence of each frame; that is, the number and position of the frame elements within example sentences. The sentence She was born about AD 460 falls in the valence pattern NP Ext, INI --, NP Dep which occurs twice in the FrameNet's annotation report for the born.v lexical unit, namely: She was born about AD 460, daughter and granddaughter of Roman and Byzantine emperors, whose family had been prominent in Roman politics for over 700 years. He was soon posted to north Africa, and never met their only child, a daughter born 8 June 1941. === Frame relations === FrameNet additionally captures relationships between different frames using relations. These include the following: Inheritance: When one frame is a more specific version of another, more abstract, parent frame. Anything that is true about the parent frame must also be true about the child frame, and a mapping is specified between the frame elements of the parent and the frame elements of the child. Perspectivization: A neutral frame is connected to a frame with a specific perspective of the same scenario. For example, Commerce_transfer-goods is considered from the perspective of the buyer in Commerce_buy and from that of the seller in Commerce_sell. Subframe: Some frames refer to complex scenarios that consist of several individual states or events that can be described by separate frames. For example, Criminal_process is composed of Arrest, Trial, and so on. Precedence: This relation captures the temporal order that holds between subframes of a complex frame. For example, within the Cycle_of_life_and_death frame, the subframe Death is preceded by the subframe Being_born. Causative and Inchoative: These two relations mark, for causative- and inchoative-aspect frames, the separate stative frame they refer to. For example, the stative Position_on_a_scale (e.g. "She had a high salary") is described by the causative Cause_change_of_scalar_position (e.g. "She raised his salary") and by the inchoative Change_position_on_a_scale frame (e.g. "Her salary increased"). Using: This relation marks a frame that in some way involves another frame. For example, Judgment_communication uses both Judgment and Statement, but does not inherit from either of them because there is no clear correspondence of frame elements. See also: Connects frames that bear some resemblance but need to be distinguished carefully. == Applications == FrameNet has proven to be useful in a number of computational applications, because computers need additional knowledge in order to recognize that "John sold a car to Mary" and "Mary bought a car from John" describe essentially the same situation, despite using two quite different verbs, different prepositions and a different word order. FrameNet has been used in applications like question answering, paraphrasing, recognizing textual entailment, and information extraction, either directly or by means of Semantic Role Labeling tools. The first automatic system for Semantic Role Labeling (SRL, sometimes also referred to as "shallow semantic parsing") was developed by Daniel Gildea and Daniel Jurafsky based on FrameNet in 2002. Semantic Role Labeling has since become one of the standard tasks in natural language processing, with the latest version (1.7) of FrameNet now fully supported in the Natural Language Toolkit. Since frames are essentially semantic descriptions, they are similar across languages, and several projects have arisen over the years that have relied on the original FrameNet as the basis for additional non-English FrameNets, for Spanish, Japanese, German, and Polish, among others.
Is an AI Analytics Tool Worth It in 2026?
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Corpus language
A corpus language is a language that has no living speakers but for which numerous records produced by its native speakers survive. Examples of corpus languages are Ancient Greek, Latin, the Egyptian language, Old English, Old Norse, Elamite, and Sanskrit. Some corpus languages, such as Ancient Greek and Latin, left very large corpora and therefore can be fully reconstructed, even though some details of pronunciation may be unclear. Such languages can be used even today, as is the case with Sanskrit and Latin. Other languages have such limited corpora that some important words—e.g., some pronouns—are lacking in the corpora. Examples of these are Ugaritic and Gothic. Languages attested only by a few words, often names, and a few phrases, are called Trümmersprache (literally "rubble languages") in German linguistics. These can be reconstructed only in a very limited way, and often their genetic relationship to other languages remains unclear. Examples are Dalmatian, Etruscan, also known as Rasenna, Dadanitic, a Semitic language that may be close to classical Arabic, Lombardic, Burgundian, Vandalic, and Oscan, Umbrian, and Faliscan, all Italic languages that were related to Latin. Corpus languages are studied using the methods of corpus linguistics, but corpus linguistics can also be used (and is commonly used) for the study of the writings and other records of living languages. Not all extinct languages are corpus languages, since there are many extinct languages in which few or no writings or other records survive, as is the case in the vast majority of languages that have ever existed.
Cooliris (plugin)
Cooliris (for Desktop), formerly known as PicLens, was a web browser extension developed by Cooliris, Inc, and later acquired by Yahoo. The plugin provides an interactive 3D-like experience for viewing digital images and videos from the web and from desktop applications. The software places a small icon atop image thumbnails that appear on a webpage. Clicking on the icon loads the Cooliris 3D Wall, a browsing environment that gives the user the effect of flying through a three-dimensional space. Released to the public in January 2008, The New York Times described Cooliris as the "new immersive approach to Web navigation". Cooliris went out to win the 2008 Crunchies Award for Best Design. The plugin has received over 50 million downloads. As of May 2014 browser plugins are unavailable from the official website. There are only links to tablet apps - for iOS and Android.
AI Headshot Generators: Free vs Paid (2026)
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