Natural language processing in the context of "Speech recognition"

A natural language or ordinary language is any spoken language or signed language used organically in a human community, first emerging without conscious premeditation and subject to: replication across generations of people in the community, regional expansion or contraction, and gradual internal and structural changes. The vast majority of languages in the world are natural languages. As a category, natural language includes both standard dialects (ones with high social prestige) as well as nonstandard or vernacular dialects. Even an official language with a regulating academy such as Standard French, overseen by the Académie Française, is still classified as a natural language (e.g. in the field of natural language processing), as its prescriptive aspects do not make it regulated enough to be considered a constructed or controlled natural language. Linguists broadly consider writing to be a static visual representation of a particular natural language, though, in many cases in highly literate modern societies, writing itself can also be considered natural language.

Excluded from the definition of natural language are: artificial and constructed languages, such as those developed for works of fiction; languages of formal logic, such as those in computer programming; and non-human communication systems in nature, such as whale vocalizations or honey bees' waggle dance. The academic consensus is that particular key features prevent animal communication systems from being classified as languages at all. Certain human communication or linguistic systems with no native speakers, as sometimes used in cross-cultural contexts, are also not natural languages.

Computer science is the study of computation, information, and automation. Included broadly in the sciences, computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). An expert in the field is known as a computer scientist.

Algorithms and data structures are central to computer science.The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

Applied linguistics is an interdisciplinary field which identifies, investigates, and offers solutions to language-related real-life problems. Some of the academic fields related to applied linguistics are education, psychology, communication research, information science, natural language processing, anthropology, and sociology. Applied linguistics is a practical use of language.

In linguistics and natural language processing, a corpus (pl.: 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.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance.

ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics.

Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.

There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

In linguistics, anaphora (/əˈnæfərə/) is the use of an expression whose interpretation depends upon another expression in context (its antecedent). 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 sense, but not in a narrower one). 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 mentioned elsewhere in a dialog or text.

Anaphora is an important concept for different reasons and on different levels: first, anaphora indicates how discourse is constructed and maintained; second, anaphora binds different syntactical elements together at the level of the sentence; third, anaphora presents a challenge to natural language processing in computational linguistics, since the identification of the reference can be difficult; and fourth, anaphora partially reveals how language is understood and processed, which is relevant to fields of linguistics interested in cognitive psychology.