Computer-assisted language learningComputer-assisted language learning (CALL), British, or Computer-Aided Instruction (CAI)/Computer-Aided Language Instruction (CALI), American, is briefly defined in a seminal work by Levy (1997: p. 1) as "the search for and study of applications of the computer in language teaching and learning". CALL embraces a wide range of information and communications technology applications and approaches to teaching and learning foreign languages, from the "traditional" drill-and-practice programs that characterised CALL in the 1960s and 1970s to more recent manifestations of CALL, e.
Language processing in the brainIn psycholinguistics, language processing refers to the way humans use words to communicate ideas and feelings, and how such communications are processed and understood. Language processing is considered to be a uniquely human ability that is not produced with the same grammatical understanding or systematicity in even human's closest primate relatives. Throughout the 20th century the dominant model for language processing in the brain was the Geschwind-Lichteim-Wernicke model, which is based primarily on the analysis of brain-damaged patients.
Part of speechIn grammar, a part of speech or part-of-speech (abbreviated as POS or PoS, also known as word class or grammatical category) is a category of words (or, more generally, of lexical items) that have similar grammatical properties. Words that are assigned to the same part of speech generally display similar syntactic behavior (they play similar roles within the grammatical structure of sentences), sometimes similar morphological behavior in that they undergo inflection for similar properties and even similar semantic behavior.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Syntactic categoryA syntactic category is a syntactic unit that theories of syntax assume. Word classes, largely corresponding to traditional parts of speech (e.g. noun, verb, preposition, etc.), are syntactic categories. In phrase structure grammars, the phrasal categories (e.g. noun phrase, verb phrase, prepositional phrase, etc.) are also syntactic categories. Dependency grammars, however, do not acknowledge phrasal categories (at least not in the traditional sense).
Language developmentLanguage development in humans is a process starting early in life. Infants start without knowing a language, yet by 10 months, babies can distinguish speech sounds and engage in babbling. Some research has shown that the earliest learning begins in utero when the fetus starts to recognize the sounds and speech patterns of its mother's voice and differentiate them from other sounds after birth. Typically, children develop receptive language abilities before their verbal or expressive language develops.
Statistical language acquisitionStatistical language acquisition, a branch of developmental psycholinguistics, studies the process by which humans develop the ability to perceive, produce, comprehend, and communicate with natural language in all of its aspects (phonological, syntactic, lexical, morphological, semantic) through the use of general learning mechanisms operating on statistical patterns in the linguistic input. Statistical learning acquisition claims that infants' language-learning is based on pattern perception rather than an innate biological grammar.
Neural networkA neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.
Object of the mindAn object of the mind is an object that exists in the imagination, but which, in the real world, can only be represented or modeled. Some such objects are abstractions, literary concepts, or fictional scenarios. Closely related are intentional objects, which are what thoughts and feelings are about, even if they are not about anything real (such as thoughts about unicorns, or feelings of apprehension about a dental appointment which is subsequently cancelled).
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.
