- "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."
It involves the use of computer algorithms and programming to study and make predictions about the structures and patterns found in dead languages.
Phonetics and Phonology: Study of the sounds and speech patterns used in language.
Morphology: Study of the internal structure of words.
Syntax: Study of the structure of sentences and phrases.
Semantics: Study of the meaning of words and sentences.
Pragmatics: Study of how language is used in context.
Natural Language Processing (NLP): A field of computer science that focuses on how machines can process and understand human language.
Machine Learning: A type of artificial intelligence that allows computers to learn from data and make predictions or decisions.
Information Retrieval: The process of retrieving relevant information from a large body of data.
Text Mining: The process of analyzing large collections of text data to discover patterns and insights.
Corpus Linguistics: The use of large collections of text data to study language patterns and usage.
Computational Semiotics: Study of meaning-making in the use of signs.
Computational Pragmatics: Understanding context by processing large amounts of data.
Linguistic Typology: Comparing and contrasting the structural properties of natural languages to understand what the commonalities and differences are.
Language Technology Industry: Developing tools such as machine translation, language modeling, and speech recognition.
Computational Psycholinguistics: Using computer models to better understand the cognitive processes underlying language use and acquisition.
Sentiment Analysis: Identifying emotions and opinions expressed in text.
Named Entity Recognition: Identifying and categorizing named entities in text.
Dialogue Systems: Building conversational agents that can understand and generate natural language responses.
Speech Processing: The technical aspects of processing speech, including speech signal processing, acoustic modeling, and speech recognition.
Multilingual NLP: Supporting the processing of multiple languages by NLP systems.
Natural Language Processing (NLP): NLP involves developing algorithms and systems that can process and understand human language, including automated translation, sentiment analysis, and speech recognition.
Corpus Linguistics: Corpus Linguistics involves analyzing large collections of language data (corpora) to identify patterns, tendencies, and relationships in language use over time.
Machine Translation: Machine Translation involves developing systems that can automatically translate text from one language to another.
Sentiment Analysis: Sentiment Analysis involves developing algorithms that can identify and analyze the emotional content of language data, allowing for deeper insights into public opinion and perception.
Speech Recognition: Speech Recognition involves developing systems that can automatically transcribe spoken language into written text.
Computational Psycholinguistics: Computational Psycholinguistics involves using computational methods and models to study how humans acquire, process, and use language.
Information Retrieval: Information Retrieval involves designing algorithms and systems that can identify and retrieve relevant information from large amounts of text data.
Text Mining: Text Mining involves extracting insights and knowledge from large amounts of unstructured text data using computational techniques.
Lexicography: Lexicography involves studying the structure and use of dictionaries and developing computational methods for creating, managing, and analyzing lexical resources.
Discourse Analysis: Discourse Analysis involves studying patterns of language use in larger contexts, such as conversations, interviews, and narratives, and developing computational methods for analyzing discourse structure and meaning.
- "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."
- No direct quote provided.
- "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."