Syntax (programming languages)In computer science, the syntax of a computer language is the rules that define the combinations of symbols that are considered to be correctly structured statements or expressions in that language. This applies both to programming languages, where the document represents source code, and to markup languages, where the document represents data. The syntax of a language defines its surface form. Text-based computer languages are based on sequences of characters, while visual programming languages are based on the spatial layout and connections between symbols (which may be textual or graphical).
Text miningText mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al.
Italic languagesThe Italic languages form a branch of the Indo-European language family, whose earliest known members were spoken on the Italian Peninsula in the first millennium BC. The most important of the ancient languages was Latin, the official language of ancient Rome, which conquered the other Italic peoples before the common era. The other Italic languages became extinct in the first centuries AD as their speakers were assimilated into the Roman Empire and shifted to some form of Latin.
Language familyA language family is a group of languages related through descent from a common ancestral language or parental language, called the proto-language of that family. The term "family" reflects the tree model of language origination in historical linguistics, which makes use of a metaphor comparing languages to people in a biological family tree, or in a subsequent modification, to species in a phylogenetic tree of evolutionary taxonomy. Linguists therefore describe the daughter languages within a language family as being genetically related.
Natural language processingNatural language processing (NLP) is an interdisciplinary subfield of linguistics and computer science. It is primarily concerned with processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.
Text corpusIn linguistics and natural language processing, a corpus (: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. In search technology, a corpus is the collection of documents which is being searched.
Australian Aboriginal languagesThe Indigenous languages of Australia number in the hundreds, the precise number being quite uncertain, although there is a range of estimates from a minimum of around 250 (using the technical definition of 'language' as non-mutually intelligible varieties) up to possibly 363. The Indigenous languages of Australia comprise numerous language families and isolates, perhaps as many as 13, spoken by the Indigenous peoples of mainland Australia and a few nearby islands.
AutoencoderAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction. Variants exist, aiming to force the learned representations to assume useful properties.
Variational autoencoderIn machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models and variational Bayesian methods. Variational autoencoders are often associated with the autoencoder model because of its architectural affinity, but with significant differences in the goal and mathematical formulation. Variational autoencoders are probabilistic generative models that require neural networks as only a part of their overall structure.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Text editorA text editor is a type of computer program that edits plain text. Such programs are sometimes known as "notepad" software (e.g. Windows Notepad). Text editors are provided with operating systems and software development packages, and can be used to change files such as s, documentation files and programming language source code. Plain text and Rich text There are important differences between plain text (created and edited by text editors) and rich text (such as that created by word processors or desktop publishing software).
Natural language generationNatural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information". While it is widely agreed that the output of any NLG process is text, there is some disagreement about whether the inputs of an NLG system need to be non-linguistic.
Text fileA text file (sometimes spelled textfile; an old alternative name is flatfile) is a kind of that is structured as a sequence of of electronic text. A text file exists stored as data within a . In operating systems such as CP/M and MS-DOS, where the operating system does not keep track of the file size in bytes, the end of a text file is denoted by placing one or more special characters, known as an (EOF) marker, as padding after the last line in a text file.
Programming languageA programming language is a system of notation for writing computer programs. Most programming languages are text-based formal languages, but they may also be graphical. They are a kind of computer language. The description of a programming language is usually split into the two components of syntax (form) and semantics (meaning), which are usually defined by a formal language. Some languages are defined by a specification document (for example, the C programming language is specified by an ISO Standard) while other languages (such as Perl) have a dominant implementation that is treated as a reference.
Plain textIn computing, plain text is a loose term for data (e.g. file contents) that represent only characters of readable material but not its graphical representation nor other objects (floating-point numbers, images, etc.). It may also include a limited number of "whitespace" characters that affect simple arrangement of text, such as spaces, line breaks, or tabulation characters.
Afroasiatic languagesThe Afroasiatic languages (or Afro-Asiatic, sometimes Afrasian), also known as Hamito-Semitic or Semito-Hamitic, are a language family (or "phylum") of about 400 languages spoken predominantly in West Asia, North Africa, the Horn of Africa, and parts of the Sahara and Sahel. Over 500 million people are native speakers of an Afroasiatic language, constituting the fourth-largest language family after Indo-European, Sino-Tibetan, and Niger–Congo. Most linguists divide the family into six branches: Berber, Chadic, Cushitic, Egyptian, Semitic, and Omotic.
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.
MultilingualismMultilingualism is the use of more than one language, either by an individual speaker or by a group of speakers. It is believed that multilingual speakers outnumber monolingual speakers in the world's population. More than half of all Europeans claim to speak at least one language other than their mother tongue; but many read and write in one language. Multilingualism is advantageous for people wanting to participate in trade, globalization and cultural openness.
Origin of languageThe origin of language (spoken and signed, as well as language-related technological systems such as writing), its relationship with human evolution, and its consequences have been subjects of study for centuries. Scholars wishing to study the origins of language must draw inferences from evidence such as the fossil record, archaeological evidence, contemporary language diversity, studies of language acquisition, and comparisons between human language and systems of communication existing among animals (particularly other primates).
