Recurrent neural networkA recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Language educationLanguage education – the process and practice of teaching a second or foreign language – is primarily a branch of applied linguistics, but can be an interdisciplinary field. There are four main learning categories for language education: communicative competencies, proficiencies, cross-cultural experiences, and multiple literacies. Increasing globalization has created a great need for people in the workforce who can communicate in multiple languages.
Language schoolA language school is a school where one studies a foreign language. Classes at a language school are usually geared towards, for example, communicative competence in a foreign language. Language learning in such schools typically supplements formal education or existing knowledge of a foreign language. Students vary widely by age, educational background, work experience. They usually have the possibility of selecting a specific course according to their language proficiency.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
English as a second or foreign languageEnglish as a second or foreign language is the use of English by speakers with different native languages. Language education for people learning English may be known as English as a foreign language (EFL), English as a second language (ESL), English for speakers of other languages (ESOL), English as an additional language (EAL), or English as a New Language (ENL). The aspect in which EFL is taught is referred to as teaching English as a foreign language (TEFL), teaching English as a second language (TESL) or teaching English to speakers of other languages (TESOL).
Foreign languageA foreign language is a language that is not an official language of, nor typically spoken in, a specific country. Native speakers from that country usually need to acquire it through conscious learning, such as through language lessons at school, self-teaching, or attending language courses. A foreign language might be learned as a second language; however, there is a distinction between the two terms. A second language refers to a language that plays a significant role in the region where the speaker lives, whether for communication, education, business, or governance.
Teaching English as a second or foreign languageTeaching English as a foreign language (TEFL), Teaching English as a second language (TESL) or Teaching English to speakers of other languages (TESOL) are terms that refer to teaching English to students whose first language is not English. The terms TEFL, TESL, and TESOL distinguish between a class's location and student population. TEFL describes English language programs that occur in countries where English is not the primary language. TEFL programs may be taught at a language school or with a tutor.
Feedforward neural networkA feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.
Types of artificial neural networksThere are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.
Residual neural networkA Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Second languageA second language (L2) is a language spoken in addition to one's first language (L1). A second language may be a neighbouring language, another language of the speaker's home country, or a foreign language. A speaker's dominant language, which is the language a speaker uses most or is most comfortable with, is not necessarily the speaker's first language. For example, the Canadian census defines first language for its purposes as "the first language learned in childhood and still spoken", recognizing that for some, the earliest language may be lost, a process known as language attrition.
Optical character recognitionOptical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).
Handwriting recognitionHandwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.
Highway networkIn machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous artificial neural networks. It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by Long Short-Term Memory (LSTM) recurrent neural networks. The advantage of a Highway Network over the common deep neural networks is that it solves or partially prevents the vanishing gradient problem, thus leading to easier to optimize neural networks.
Second-language acquisitionSecond-language acquisition (SLA), sometimes called second-language learning — otherwise referred to as L2 (language 2) acquisition, is the process by which people learn a second language. Second-language acquisition is also the scientific discipline devoted to studying that process. The field of second-language acquisition is regarded by some but not everybody as a sub-discipline of applied linguistics but also receives research attention from a variety of other disciplines, such as psychology and education.
Target marketA target market, also known as serviceable obtainable market (SOM), is a group of customers within a business's serviceable available market at which a business aims its marketing efforts and resources. A target market is a subset of the total market for a product or service. The target market typically consists of consumers who exhibit similar characteristics (such as age, location, income or lifestyle) and are considered most likely to buy a business's market offerings or are likely to be the most profitable segments for the business to service by OCHOM Once the target market(s) have been identified, the business will normally tailor the marketing mix (4 Ps) with the needs and expectations of the target in mind.
Language pedagogyLanguage pedagogy is the discipline concerned with the theories and techniques of teaching language. It has been described as a type of teaching wherein the teacher draws from their own prior knowledge and actual experience in teaching language. The approach is distinguished from research-based methodologies. There are several methods in language pedagogy but they can be classified into three: structural, functional, and interactive. Each of these encompasses a number of methods that can be utilised in order to teach and learn languages.
Pattern recognitionPattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Speech recognitionSpeech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.
