May 1, 2024
4 minute read
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages. NLP is a challenging field as natural language is highly ambiguous and often imprecise. As a subfield of computer science, NLP deals with how to program computers to process and analyze large amounts of natural language data. Such data can come from a variety of sources, including text, speech, and sign language. NLP is used in a wide range of applications, including machine translation, text summarization, question answering, and chatbots.
Subfields of NLP
There are many different subfields of NLP, each with its own focus. Some of the most common subfields include:
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Machine translation: The task of translating text from one language to another.
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Text summarization: The task of creating a concise summary of a text.
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Question answering: The task of answering questions about a text.
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Chatbots: The task of creating computer programs that can engage in conversation with humans.
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Named entity recognition: The task of identifying and classifying named entities (e.g., people, places, organizations) in text.
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Part-of-speech tagging: The task of assigning grammatical tags (e.g., noun, verb, adjective) to words in a sentence.
Applications of NLP
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Find a path to becoming a Natural Language Toolkit. Learn more at:
OpenCourser.com/topic/eg8o4x/natural
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Natural Language Toolkit.
Provides a comprehensive overview of speech and language processing, covering topics such as phonetics, phonology, morphology, syntax, semantics, and pragmatics. It valuable resource for students and researchers in the field.
Provides a comprehensive overview of computational linguistics and natural language processing, covering topics such as morphology, syntax, semantics, and pragmatics. It valuable resource for students and researchers in the field.
Provides a practical introduction to the Natural Language Toolkit (NLTK), a Python library for natural language processing. It covers topics such as tokenization, stemming, parsing, and semantic analysis.
Provides a practical introduction to natural language processing (NLP), using Python as the programming language. It covers topics such as tokenization, stemming, parsing, and semantic analysis.
Provides a comprehensive overview of deep learning techniques for natural language processing (NLP), covering topics such as word embeddings, convolutional neural networks, and recurrent neural networks.
Provides a practical introduction to natural language processing (NLP) using transformers, a type of deep learning model that has revolutionized the field. It covers topics such as tokenization, stemming, parsing, and semantic analysis.
Provides a practical introduction to natural language processing (NLP) using C++, a programming language that is widely used in the field. It covers topics such as tokenization, stemming, parsing, and semantic analysis.
Provides a practical introduction to natural language processing (NLP) using Haskell, a programming language that is widely used in the field. It covers topics such as tokenization, stemming, parsing, and semantic analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/eg8o4x/natural
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