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It typically supports multiple users in a collaborative environment.\r\nCMS features vary widely. Most CMSs include Web-based publishing, format management, history editing and version control, indexing, search and retrieval. By their nature, content management systems support the separation of content and presentation.\r\nContent management software solutions are typically used for enterprise content management systems (ECM) and web site content management systems (WCM). An ECM facilitates collaboration in the workplace by integrating document management, digital asset management and records retention functionalities, and providing end users with role-based access to the organization's digital assets. A WCM facilitates collaborative authoring for websites. ECM software often includes a WCM publishing functionality, but ECM webpages typically remain behind the organization's firewall.\r\nBoth enterprise content management and web content management systems have two components: a content management application (CMA) and a content delivery application (CDA). The CMA is a graphical user interface (GUI) that allows the user to control the design, creation, modification and removal of content from a website without needing to know anything about HTML. The CDA component provides the back-end services that support management and delivery of the content once it has been created in the CMA.\r\nDigital asset management systems are another type of CMS. They manage content with a clearly defined author or ownership, such as documents, movies, pictures, phone numbers, and scientific data. Companies also use CMSs to store, control, revise, and publish documentation.\r\nBased on market share statistics, the most popular CMS is WordPress, used by more than 28% of all websites on the Internet, and by 59% of all websites using a known content management system, followed by Joomla and Drupal.\r\n<span style=\"font-weight: bold;\">Content management systems typically provide the following features:</span>\r\n<ul><li>Search engine optimization</li><li>Integrated and online documentation</li><li>Modularity and extensibility</li><li>User and group functionality</li><li>Templating support for changing designs</li><li>Installation and upgrade wizards</li><li>Integrated audit logs</li><li>Compliance with various accessibility frameworks and standards, such as WAI-ARIA</li><li>Reduced need to code from scratch</li><li>Unified user experience</li><li>Version control</li><li>Edit permission management</li></ul>","materialsDescription":"<h1 class=\"align-center\"> <span style=\"font-weight: normal;\">What is a CMS?</span></h1>\r\nAnswer: CMS is an acronym for &quot;Content Management System&quot;. You may see some variations on this term, but they all refer to the same concept. Variations include:\r\n<ul><li>Content Management System</li><li>Web CMS</li><li>Web Content Management System</li><li>CMS Platform</li><li>Content Management Platform</li><li>CMS System</li></ul>\r\n<h1 class=\"align-center\"><span style=\"font-weight: normal;\">What does a CMS do?</span></h1>\r\n<p class=\"align-left\">In it's simplest terms, Content Management Systems are designed to help users create and manage their websites. Content management solutions help webmasters manage the many different resources, content types and various data that make up modern web sites.</p>\r\n<p class=\"align-left\">At a minimum, modern websites make use of HTML, CSS, JavaScript and images (jpeg, gif, png, etc) to create web content for visitors to read. At the core of every CMS is the ability to organize these resources and generate valid content that can be read by web browsers. </p>\r\n<p class=\"align-left\">More advanced websites have interactive components (comment sections, forums, e-commerce...) that requires server software to validate and save user submitted content.<br />All of the top CMS platforms have features built-in or available for download as addons for all of these features.</p>\r\n<h1 class=\"align-center\"><span style=\"font-weight: normal;\">What are the main types of CMS?</span></h1>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Simple CMS.</span> This system is used to create simple websites that contain several pages using simple control systems. Simple content management systems consist of several modules that are set one time. These CMSs are free and are available on the internet. Among their disadvantages are the inability to change settings, low transmission capacity, inability to create pages dynamically and the inability of ato delegateion of administrator’s credentials to others.<span style=\"font-weight: bold;\"></span></p>\r\n<p class=\"align-left\">&nbsp;</p>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Template CMS.</span> It consists of modules as well, but its structure is more complex if compared to a simple CMS. Template CMS has high transmission capacity, around 50,000 inquiries. Also, it has the support of dynamic pages and the ability to delegate the administrator’s credentials. Many template systems are used to create website content because they are easy to use.<span style=\"font-weight: bold;\"></span></p>\r\n<p class=\"align-left\">&nbsp;</p>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Professional CMS</span>. This type of CMS has a higher level of complexity. You may change the structure of internet resources. Additional modules can be attached to these systems. These systems are used to create information portals or massive projects. As a rule, these CMSs are a paid resource.<span style=\"font-weight: bold;\"></span></p>\r\n<p class=\"align-left\">&nbsp;</p>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Universal CMS</span>. Universal systems have wide functionality and ample opportunities to develope websites of any complexity. They support the functions of changing the structure, creating dynamic pages, modification of settings and credential distribution. Universal CMS is quite expensive. These CMSs are used for work with large portals and web-projects that require high functionality and dynamics.<br /><br /><br /><br /><br /><br /></p>","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/CMS_-_content_management_system.png","alias":"cms-content-management-system"},"283":{"id":283,"title":"Content Analytics, Discovery, and Cognitive Software","description":"<span style=\"font-weight: bold; \">Content Analytics</span> comprises of range of search and reporting technologies which can provide identical levels of business intelligence and strategic value for unstructured data. Content analytics for unstructured information includes social media monitoring, reputation monitoring, and sentiment analysis. <span style=\"font-weight: bold; \">Content analytics software</span> uses natural language queries, trends analysis, predictive analytics and contextual discovery to reveal trends and patterns for the company's unstructured data.\r\n<span style=\"font-weight: bold; \">Discovery tools</span> are the search tools that analyse contents for its likely relevancy to a process, by linking names, time periods or terms used. Discovery tools can also extend to legal hold and partitioning of content for further scrutiny. Various types of discovery tools include search engines, auto-categorization, and information visualization tools.\r\n<span style=\"font-weight: bold;\">Cognitive computing software</span> makes context computable by identifying and extracting context features such as hour, location, task, history or profile in structured way for an individual or an application engaged in a specific process at definite time and place. Cognitive computing technologies and platforms includes expert assistance software.\r\n<span style=\"font-weight: bold;\">Content Analytics, Discovery and Cognitive Systems,</span> collectively covers the market and technologies that access, analyse, organize, and provides advisory services related to a range of unstructured data. Cognitive systems leverages large amounts of structured and unstructured data and content analytics tools, along with several infrastructure technologies to answer questions, provide recommendations and directions. ","materialsDescription":"<h1 class=\"align-center\">What are Content Analytics?</h1>\r\nContent analytics is one way that brands can measure the success of their digital content; it involves analyzing data from both inbound and outbound collateral to figure out what’s working and what’s not. After all, no one wants to spend hundreds or even thousands of dollars on content that isn’t resonating.\r\nIt should come as no surprise, then, that 75 percent of enterprises think content analytics can provide real business insight: What are your customers most interested in reading about? What format generates the most engagement? You can use answers to these questions to help you decide what content you should be producing, how to present it (quiz, paid ad, slideshow, blog post, etc.), and where/when to post it for the best results. If utilized correctly, content analytics can offer brands a way to build customer loyalty and boost revenue.\r\nAdvanced content analytics strategies should help you to look beyond the numbers and to focus on taking action. Content analytics is not a mirror reflecting how your company’s content is performing; rather, it is but one tool that can help steer you towards actionable solutions and more effective content choices.\r\nMetrics provided by content analytics, and the actions you take based off that data, can help you optimize conversion rates. In addition to tweaking your content strategy, you can also build a conversion optimization strategy by testing calls-to-action (CTAs), headlines, subscription forms, etc.\r\n<h1 class=\"align-center\"><span style=\"font-weight: bold; \">What are the features of a cognitive computing solution?</span></h1>\r\nWith the present state of cognitive function computing, basic solution can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. Virtual advisor such as Dr. AI by HealthTap is a cognitive solution. It relies on individual patients’ medical profiles and knowledge gleaned from 105,000 physicians. It compiles a prioritized list of the symptoms and connects to a doctor if required. Now, experts are working on implementing cognitive solutions in enterprise systems. Some use cases are fraud detection using machine learning, predictive analytics solution, predicting oil spills in Oil and Gas production cycle etc.\r\nThe purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention. In order to implement cognitive function computing in commercial and widespread applications, Cognitive Computing consortium has recommended the following features for the computing systems:\r\n<span style=\"font-weight: bold; \">Adaptive.</span>This is the first step in making a machine learning based cognitive system. The solutions should mimic the ability of the human brain to learn and adapt from the surroundings. The systems can’t be programmed for an isolated task. It needs to be dynamic in data gathering, understanding goals, and requirements.\r\n<span style=\"font-weight: bold; \">Interactive.</span>Similar to brain the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bi-directionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some skilled intelligent chatbots such as Mitsuku have already achieved this feature.\r\n<span style=\"font-weight: bold; \">Iterative and stateful.</span>The system should “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. It should be able to define the problem by asking questions or finding an additional source. This feature needs a careful application of the data quality and validation methodologies in order to ensure that the system is always provided with enough information and that the data sources it operates on to deliver reliable and up-to-date input.\r\n<span style=\"font-weight: bold; \">Contextual.</span>They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). ","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Content_Analytics_Discovery_and_Cognitive_Software.png","alias":"content-analytics-discovery-and-cognitive-software"},"429":{"id":429,"title":"Machine Translation","description":" Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.\r\nOn a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. Solving this problem with corpus statistical, and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.\r\nCurrent machine translation software often allows for customization by domain or profession (such as weather reports), improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text.\r\nImproved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are proper names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).\r\nThe progress and potential of machine translation have been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality, first and most notably by Yehoshua Bar-Hillel. Some critics claim that there are in-principle obstacles to automating the translation process.","materialsDescription":" <span style=\"font-weight: bold; \">What is Machine Translation?</span>\r\nMachine translation (MT) is an automated translation by computer software. MT can be used to translate entire texts without any human input or can be used alongside human translators. The concept of MT started gaining traction in the early 50s and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.\r\n<span style=\"font-weight: bold; \">How Does Machine Translation Work?</span>\r\nWell, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.\r\nRule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.\r\nStatistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource-intensive. When using domain-specific data, SMT can produce good quality translations, especially in the technical, medical, and financial fields.\r\nNeural MT (NMT) is a new approach that is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.\r\n<span style=\"font-weight: bold; \">What are the Pros and Cons of Machine Translation?</span>\r\n<ul><li>MT is incredibly fast and can translate thousands of words per minute.</li><li>It can translate into multiple languages at once which drastically reduces the amount of manpower needed.</li><li>Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.</li><li>MT technology is developing rapidly and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.</li></ul>\r\nThere are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.\r\n<span style=\"font-weight: bold; \">When Should You Use Machine Translation?</span>\r\nWhen translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.\r\nFor large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.\r\nUsing a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.\r\n<span style=\"font-weight: bold;\">Which MT Engine Should You Use?</span>\r\nNot all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content type.\r\nUltimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.\r\n<span style=\"font-weight: bold;\">Using Machine Translation and a Translation Management System</span>\r\nYou can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS.&nbsp; With these technologies integrated, you will be able to leverage additional tools such as translation memories, termbases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Machine_Translation.png","alias":"machine-translation"},"431":{"id":431,"title":"Electronic dictionaries","description":" An electronic dictionary is a dictionary whose data exists in digital form and can be accessed through a number of different media. Electronic dictionaries can be found in several forms, including software installed on tablet or desktop computers, mobile apps, web applications, and as a built-in function of E-readers. They may be free or require payment.\r\nMost of the early electronic dictionaries were, in effect, print dictionaries made available in digital form: the content was identical, but the electronic editions provided users with more powerful search functions. But soon the opportunities offered by digital media began to be exploited. Two obvious advantages are that limitations of space (and the need to optimize its use) become less pressing, so additional content can be provided; and the possibility arises of including multimedia content, such as audio pronunciations and video clips.\r\nElectronic dictionary databases, especially those included with software dictionaries are often extensive and can contain up to 500,000 headwords and definitions, verb conjugation tables, and a grammar reference section. Bilingual electronic dictionaries and monolingual dictionaries of inflected languages often include an interactive verb conjugator, and are capable of word stemming and lemmatization.\r\nPublishers and developers of electronic dictionaries may offer native content from their own lexicographers, licensed data from print publications, or both, as in the case of Babylon offering premium content from Merriam Webster, and Ultralingua offering additional premium content from Collins, Masson, and Simon &amp; Schuster, and Paragon Software offering original content from Duden, Britannica, Harrap, Merriam-Webster and Oxford. ","materialsDescription":" <span style=\"font-weight: bold;\">What are the capabilities of electronic dictionaries?</span>\r\nCompared with printed analogues, computer dictionaries provide the user with many additional features:\r\n<ul><li>multiple increase in search speed;</li><li>a set of entries in the dictionary: vocabulary, alphabetical index, entering words and phrases from the keyboard, from a text editor;</li><li>search for words with insufficient spelling;</li><li>full-text search (not only in the vocabulary, but also in the texts of all dictionary entries);</li><li>the use of multimedia tools for vocabulary semantization;</li><li>the presence of a hyperlink system;</li><li>the presence of cross-references to all words in the dictionary;</li><li>the ability to store a large amount of information;</li><li>in bilingual dictionaries - the possibility of direct and reverse translation;</li><li>the inclusion in the structure of a computer dictionary of several dictionaries of different types and genres;</li><li>simultaneous search in several dictionaries;</li><li>restriction of the search area by keywords, thematic groups, parts of speech, etc.;</li><li> replenishing the dictionary by the user, or creating a custom dictionary;</li><li>preservation of the search sequence during the session (the so-called chronology/search history);</li><li>saving &quot;bookmarks&quot; in the dictionary;</li><li> compatibility with text editors, the ability to copy dictionary entries and access to the dictionary from the editor;</li><li>compatibility with machine translation programs;</li><li>compatibility with web browsers and other types of programs (application, training, gaming);</li><li>providing additional background information on phonetics, grammar, style and other aspects of the language;</li><li>the possibility of using dictionaries in local and global networks, etc.</li></ul>","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Electronic_dictionaries.png","alias":"electronic-dictionaries"},"433":{"id":433,"title":"Spelling Check Systems","description":" In software, a spell checker (or spell check) is a software feature that checks for misspellings in a text. Spell-checking features are often embedded in software or services, such as a word processor, email client, electronic dictionary, or search engine.\r\nA basic spell checker carries out the following processes:\r\n<ul><li>It scans the text and extracts the words contained in it.</li><li>It then compares each word with a known list of correctly spelled words (i.e. a dictionary). This might contain just a list of words, or it might also contain additional information, such as hyphenation points or lexical and grammatical attributes.</li><li>An additional step is a language-dependent algorithm for handling morphology. Even for a lightly inflected language like English, the spell-checker will need to consider different forms of the same word, such as plurals, verbal forms, contractions, and possessives. For many other languages, such as those featuring agglutination and more complex declension and conjugation, this part of the process is more complicated.</li></ul>\r\nIt is unclear whether morphological analysis — allowing for many different forms of a word depending on its grammatical role—provides a significant benefit for English, though its benefits for highly synthetic languages such as German, Hungarian or Turkish are clear.\r\nAs an adjunct to these components, the program's user interface will allow users to approve or reject replacements and modify the program's operation.\r\nAn alternative type of spell checker uses solely statistical information, such as n-grams, to recognize errors instead of correctly-spelled words. This approach usually requires a lot of effort to obtain sufficient statistical information. Key advantages include needing less runtime storage and the ability to correct errors in words that are not included in a dictionary.\r\nIn some cases spell checkers use a fixed list of misspellings and suggestions for those misspellings; this less flexible approach is often used in paper-based correction methods, such as the see also entries of encyclopedias.\r\nClustering algorithms have also been used for spell checking combined with phonetic information.","materialsDescription":" <span style=\"font-weight: bold;\">What is the functionality of spelling check systems?</span>\r\nThe first spell checkers were &quot;verifiers&quot; instead of &quot;correctors.&quot; They offered no suggestions for incorrectly spelled words. This was helpful for typos but it was not so helpful for logical or phonetic errors. The challenge the developers faced was the difficulty in offering useful suggestions for misspelled words. This requires reducing words to a skeletal form and applying pattern-matching algorithms.\r\nIt might seem logical that where spell-checking dictionaries are concerned, &quot;the bigger, the better,&quot; so that correct words are not marked as incorrect. In practice, however, an optimal size for English appears to be around 90,000 entries. If there are more than this, incorrectly spelled words may be skipped because they are mistaken for others. For example, a linguist might determine on the basis of corpus linguistics that the word baht is more frequently a misspelling of bath or bat than a reference to the Thai currency. Hence, it would typically be more useful if a few people who write about Thai currency were slightly inconvenienced than if the spelling errors of the many more people who discuss baths were overlooked.\r\nThe first MS-DOS spell checkers were mostly used in proofing mode from within word processing packages. After preparing a document, a user scanned the text looking for misspellings. Later, however, batch processing was offered in such packages as Oracle's short-lived CoAuthor and allowed a user to view the results after a document was processed and correct only the words that were known to be wrong. When memory and processing power became abundant, spell checking was performed in the background in an interactive way, such as has been the case with the Sector Software produced Spellbound program released in 1987 and Microsoft Word since Word 95.\r\nIn recent years, spell checkers have become increasingly sophisticated; some are now capable of recognizing simple grammatical errors. However, even at their best, they rarely catch all the errors in a text (such as homophone errors) and will flag neologisms and foreign words as misspellings. Nonetheless, spell checkers can be considered as a type of foreign language writing aid that non-native language learners can rely on to detect and correct their misspellings in the target language.\r\n<span style=\"font-weight: bold;\">Spell-checking non-English languages</span>\r\nEnglish is unusual in that most words used in formal writing have a single spelling that can be found in a typical dictionary, with the exception of some jargon and modified words. In many languages, words are often concatenated into new combinations of words. In German, compound nouns are frequently coined from other existing nouns. Some scripts do not clearly separate one word from another, requiring word-splitting algorithms. Each of these presents unique challenges to non-English language spell checkers.\r\n<span style=\"font-weight: bold;\">Context-sensitive spell checkers</span>\r\nThere has been researched on developing algorithms that are capable of recognizing a misspelled word, even if the word itself is in the vocabulary, based on the context of the surrounding words. Not only does this allow words such as those in the poem above to be caught, but it mitigates the detrimental effect of enlarging dictionaries, allowing more words to be recognized. For example, baht in the same paragraph as Thai or Thailand would not be recognized as a misspelling of a bath. The most common example of errors caught by such a system are homophone errors, such as the bold words in the following sentence:\r\n\r\n<blockquote><span style=\"font-weight: bold;\">Their</span> coming <span style=\"font-weight: bold;\">too sea</span> if <span style=\"font-weight: bold;\">its reel</span>.\r\n</blockquote>\r\nThe most successful algorithm to date is Andrew Golding and Dan Roth's &quot;Winnow-based spelling correction algorithm&quot;, published in 1999, which is able to recognize about 96% of context-sensitive spelling errors, in addition to ordinary non-word spelling errors. A context-sensitive spell checker appears in Microsoft Office 2007 and also appeared in the now-defunct Google Wave.\r\nGrammar checkers attempt to fix problems with grammar beyond spelling errors, including incorrect choice of words.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Spelling_Check_Systems.png","alias":"spelling-check-systems"}},"companyUrl":"http://www.langsoft.ch","countryCodes":["CHE","USA"],"certifications":[],"isSeller":true,"isSupplier":false,"isVendor":true,"presenterCodeLng":"","seo":{"title":"Langsoft - Sprachlertnmittel","keywords":"","description":"Natural Language Text Processing, Natural Language Understanding, Automated Reasoning in Natural Language software development with commercial software products for Windows PC, encompassing all contemporary spheres of Natural Language Text Processing: Parsing,","og:title":"Langsoft - Sprachlertnmittel","og:description":"Natural Language Text Processing, Natural Language Understanding, Automated Reasoning in Natural Language software development with commercial software products for Windows PC, encompassing all contemporary spheres of Natural Language Text Processing: Parsing,","og:image":"https://old.roi4cio.com/uploads/roi/company/chatbots_1.jpg"},"eventUrl":"","vendorPartners":[],"supplierPartners":[],"vendoredProducts":[],"suppliedProducts":[],"partnershipProgramme":null}},"aliases":{},"links":{},"meta":{},"loading":false,"error":null},"implementations":{"implementationsByAlias":{},"aliases":{},"links":{},"meta":{},"loading":false,"error":null},"agreements":{"agreementById":{},"ids":{},"links":{},"meta":{},"loading":false,"error":null},"comparison":{"loading":false,"error":false,"templatesById":{},"comparisonByTemplateId":{},"products":[],"selectedTemplateId":null},"presentation":{"type":null,"company":{},"products":[],"partners":[],"formData":{},"dataLoading":false,"dataError":false,"loading":false,"error":false},"catalogsGlobal":{"subMenuItemTitle":""}}