Computational linguistics

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The application of computational techniques to the study of language, including natural language processing, machine translation, and speech recognition.

Phonetics and Phonology: The study of speech sounds and their characteristics in language. This includes the production, perception and classification of sounds, as well as the rules governing their use within a language.
Morphology: The study of the internal structure of words and how they are formed from smaller units known as morphemes.
Syntax: The study of the structure and organization of sentences and phrases in language, including the rules for forming and combining them.
Semantics: The study of meaning in language, including how words and phrases are used to convey meaning and how they relate to one another.
Pragmatics: The study of how language is used in context, including the social and cultural factors that influence communication.
Corpus Linguistics: The study of large collections of text or speech data, with the aim of uncovering patterns and regularities in language use.
Machine Learning: Techniques for developing algorithms that can learn from data, including supervised and unsupervised learning, deep learning and neural networks.
Natural Language Processing (NLP): The use of computer algorithms and mathematical models to analyze, understand and generate human language.
Computational Modeling: The use of computer simulations and models to represent language and the cognitive processes involved in language use.
Linguistic Data Science: The application of data science techniques and methods to linguistic data, with the aim of gaining insights into language use and improving NLP algorithms.
Natural Language Processing (NLP): It is a branch of AI that deals with the interaction between humans and computers using natural language.
Automated Translation: It involves the use of algorithms to translate from one language to another automatically.
Text-to-speech (TTS): It involves the conversion of written text into spoken words using computational algorithms.
Speech-to-Text (STT): It involves the conversion of spoken words into written text using computational algorithms.
Sentiment Analysis: It involves the use of algorithms to identify and analyze the emotions and opinions present in a given text.
Language Generation: It involves the use of computational algorithms to generate written or spoken language.
Text Summarization: It involves the use of computational algorithms to summarize large volumes of text into shorter, more concise versions.
Named-entity recognition (NER): It is a subfield of information extraction that seeks to locate and classify named entities mentioned in unstructured text.
Part-of-speech tagging (POS): It is a process of marking up a word in a text (corpus) as corresponding to a particular part of speech.
Information Retrieval: It deals with the search and retrieval of relevant information from unstructured data, such as documents and texts.
- "Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions."
- "Computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others."
- "Since the 2020s, computational linguistics has become a near-synonym of either natural language processing or language technology."
- "Deep learning approaches, such as large language models, outperform the specific approaches previously used in the field."
- "Computational linguistics is concerned with the computational modelling of natural language."
- "Linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology, and neuroscience."
- "Computational approaches are studied to find appropriate solutions to linguistic questions."
- "Computational linguistics draws upon linguistics, computer science, and artificial intelligence."
- "Cognitive science, cognitive psychology, and psycholinguistics."
- "Philosophy is one of the disciplines that computational linguistics draws upon."
- "Deep learning approaches, such as large language models, outperform the specific approaches previously used in the field."
- "Since the 2020s, computational linguistics has become a near-synonym of either natural language processing or language technology."
- "Computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology, and neuroscience."
- "Computational linguistics draws upon... neuroscience."
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- "Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language."
- "Deep learning approaches, such as large language models, outperform the specific approaches previously used in the field."
- No direct quote provided.
- "Computational linguistics is concerned with the computational modelling of natural language."
- "Since the 2020s, computational linguistics has become a near-synonym of either natural language processing or language technology, with deep learning approaches, such as large language models, outperforming the specific approaches previously used in the field."