Natural Language Generation
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Natural Language Generation
Natural language generation (NLG) is a software process that produces natural language output. In one of the most widely-cited survey of NLG methods, NLG is characterized 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 on whether the inputs of an NLG system need to be non-linguistic. Common applications of NLG methods include the production of various reports, for example weather and patient reports; image captions; and chatbots. Automated NLG can be compared to the process humans use when they turn ideas into writing or speech. Psycholinguists prefer the term language production for this process, which can also be described in mathematical terms, or modeled in ...
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Natural Language
In neuropsychology, linguistics, and philosophy of language, a natural language or ordinary language is any language that has evolved naturally in humans through use and repetition without conscious planning or premeditation. Natural languages can take different forms, such as speech or signing. They are distinguished from constructed and formal languages such as those used to program computers or to study logic. Defining natural language Natural language can be broadly defined as different from * artificial and constructed languages, e.g. computer programming languages * constructed international auxiliary languages * non-human communication systems in nature such as whale and other marine mammal vocalizations or honey bees' waggle dance. All varieties of world languages are natural languages, including those that are associated with linguistic prescriptivism or language regulation. ( Nonstandard dialects can be viewed as a wild type in comparison with standard l ...
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Lexical Choice
Lexical choice is the subtask of Natural language generation that involves choosing the content words (nouns, verbs, adjectives, and adverbs) in a generated text. Function words (determiners, for example) are usually chosen during realisation. Examples The simplest type of lexical choice involves mapping a domain concept (perhaps represented in an ontology) to a word. For example, the concept Finger might be mapped to the word ''finger''. A more complex situation is when a domain concept is expressed using different words in different situations. For example, the domain concept Value-Change can be expressed in many ways * ''The temperature rose'': the verb ''rose'' is used for a Value-Change in temperature which increases the value * ''The temperature fell'': the verb ''fell'' is used for a Value-Change in temperature which decreases the value * ''The rain got heavier'': the phrase ''got heavier'' is used for a Value-Change in precipitation amount when the precipitation is rain. ...
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Met Office
The Meteorological Office, abbreviated as the Met Office, is the United Kingdom's national weather service. It is an executive agency and trading fund of the Department for Business, Energy and Industrial Strategy and is led by CEO Penelope Endersby, who took on the role as Chief Executive in December 2018 and is the first woman to do so. The Met Office makes meteorological predictions across all timescales from weather forecasts to climate change. History The Met Office was established on 1 August 1854 as a small department within the Board of Trade under Vice Admiral (Royal Navy), Vice Admiral Robert FitzRoy as a service to sailor, mariners. The loss of the passenger vessel, the Royal Charter (ship), ''Royal Charter'', and 459 lives off the coast of Anglesey in a violent storm in October 1859 led to the first gale warning service. FitzRoy established a network of 15 coastal stations from which visual gale warnings could be provided for ships at sea. The new electric tele ...
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Data Analysis
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering ne ...
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Automatic Summarization
Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (''key-frames''), and/or the most important video segm ...
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Automatic Image Annotation
Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations, then techniques were developed using machine translation to try to translate the textual vocabulary with the 'visual vocabulary', or clustered regions known as ''blobs''. Work following these e ...
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Long Short-term Memory
Long short-term memory (LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process not only single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, robot control, video games, and healthcare. The name of LSTM refers to the analogy that a standard RNN has both "long-term memory" and "short-term memory". The connection weights and biases in the network change once per episode of training, analogous to how physiological changes in synaptic strengths store long-term memories; the activation patterns in the network change once per time-step, analogous to how the moment-to-moment change in electric firing patterns in the brain store short- ...
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Orthography
An orthography is a set of conventions for writing a language, including norms of spelling, hyphenation, capitalization, word breaks, emphasis, and punctuation. Most transnational languages in the modern period have a writing system, and most of these systems have undergone substantial standardization, thus exhibiting less dialect variation than the spoken language. These processes can fossilize pronunciation patterns that are no longer routinely observed in speech (e.g., "would" and "should"); they can also reflect deliberate efforts to introduce variability for the sake of national identity, as seen in Noah Webster's efforts to introduce easily noticeable differences between American and British spelling (e.g., "honor" and "honour"). Some nations (e.g. France and Spain) have established language academies in an attempt to regulate orthography officially. For most languages (including English) however, there are no such authorities and a sense of 'correct' orthography evol ...
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Morphology (linguistics)
In linguistics, morphology () is the study of words, how they are formed, and their relationship to other words in the same language. It analyzes the structure of words and parts of words such as stems, root words, prefixes, and suffixes. Morphology also looks at parts of speech, intonation and stress, and the ways context can change a word's pronunciation and meaning. Morphology differs from morphological typology, which is the classification of languages based on their use of words, and lexicology, which is the study of words and how they make up a language's vocabulary. While words, along with clitics, are generally accepted as being the smallest units of syntax, in most languages, if not all, many words can be related to other words by rules that collectively describe the grammar for that language. For example, English speakers recognize that the words ''dog'' and ''dogs'' are closely related, differentiated only by the plurality morpheme "-s", only found bound to noun ...
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Syntax
In linguistics, syntax () is the study of how words and morphemes combine to form larger units such as phrases and sentences. Central concerns of syntax include word order, grammatical relations, hierarchical sentence structure ( constituency), agreement, the nature of crosslinguistic variation, and the relationship between form and meaning (semantics). There are numerous approaches to syntax that differ in their central assumptions and goals. Etymology The word ''syntax'' comes from Ancient Greek roots: "coordination", which consists of ''syn'', "together", and ''táxis'', "ordering". Topics The field of syntax contains a number of various topics that a syntactic theory is often designed to handle. The relation between the topics is treated differently in different theories, and some of them may not be considered to be distinct but instead to be derived from one another (i.e. word order can be seen as the result of movement rules derived from grammatical relations). Se ...
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Realization (linguistics)
In linguistics, realization is the process by which some kind of surface representation is derived from its underlying representation; that is, the way in which some abstract object of linguistic analysis comes to be produced in actual language. Phonemes are often said to be ''realized'' by speech sounds. The different sounds that can realize a particular phoneme are called its allophones. Realization is also a subtask of natural language generation, which involves creating an actual text in a human language (English, French, etc.) from a syntactic representation. There are a number of software packages available for realization, most of which have been developed by academic research groups in NLG. The remainder of this article concerns realization of this kind. Example For example, the following Java code causes the simplenlg systeA Gatt and E Reiter (2009). SimpleNLG: A realisation engine for practical applications. ''Proceedings of ENLG09'/ref> to print out the text ''The wo ...
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Anaphora (linguistics)
In linguistics, anaphora () is the use of an expression whose interpretation depends upon another expression in context (its antecedent or postcedent). In a narrower sense, anaphora is the use of an expression that depends specifically upon an antecedent expression and thus is contrasted with cataphora, which is the use of an expression that depends upon a postcedent expression. The anaphoric (referring) term is called an anaphor. For example, in the sentence ''Sally arrived, but nobody saw her'', the pronoun ''her'' is an anaphor, referring back to the antecedent ''Sally''. In the sentence ''Before her arrival, nobody saw Sally'', the pronoun ''her'' refers forward to the postcedent ''Sally'', so ''her'' is now a ''cataphor'' (and an anaphor in the broader, but not the narrower, sense). Usually, an anaphoric expression is a pro-form or some other kind of deictic (contextually dependent) expression. Both anaphora and cataphora are species of endophora, referring to something menti ...
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