3. Fundamentals of Language Processing
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The fundamentals of language processing
The fundamentals of language processing in AI are the basic principles that form the basis of natural language processing (NLP) systems. NLP systems are used to analyze and understand human language, and they are used in a variety of applications, such as machine translation, speech recognition, and text analysis.
The following are some of the key concepts in language processing in AI:
Tokenization: Tokenization is the process of breaking down a string of text into its component parts, called tokens. Tokens are the basic building blocks of language, and they can be words, numbers, punctuation marks, or other symbols.
Part-of-speech tagging: Part-of-speech tagging is the process of assigning a part of speech to each token in a sentence. The parts of speech typically include nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunctions, and interjections.
Named entity recognition: Named entity recognition is the process of identifying and classifying named entities in a text. Named entities are typically people, organizations, locations, and dates.
Semantic parsing: Semantic parsing is the process of determining the meaning of a sentence or phrase. This involves identifying the different concepts in the sentence, as well as the relationships between those concepts.
Coreference resolution: Coreference resolution is the process of identifying and resolving references to the same entity in a text. For example, in the sentence "John saw Mary and she smiled," the pronouns "she" and "Mary" refer to the same entity.
Sentiment analysis: Sentiment analysis is the process of determining the sentiment of a piece of text. This involves identifying the emotional tone of the text, such as positive, negative, or neutral.
These are just some of the key concepts in language processing in AI. Language processing is a complex and challenging field, but it is also a very rewarding one. Language processing techniques are used in a variety of applications, and they are likely to have an even greater impact on our lives in the years to come.
Here are some additional resources that you may find helpful:
Natural Language Processing with Python: https://www.nltk.org/book/ by Steven Bird, Ewan Klein, and Edward Loper
Speech and Language Processing: https://web.stanford.edu/~jurafsky/slp3/ by Dan Jurafsky and Christopher Manning
The Stanford NLP Group: https://nlp.stanford.edu/
The following are some of the key concepts in language processing in AI:
Tokenization: Tokenization is the process of breaking down a string of text into its component parts, called tokens. Tokens are the basic building blocks of language, and they can be words, numbers, punctuation marks, or other symbols.
Part-of-speech tagging: Part-of-speech tagging is the process of assigning a part of speech to each token in a sentence. The parts of speech typically include nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunctions, and interjections.
Named entity recognition: Named entity recognition is the process of identifying and classifying named entities in a text. Named entities are typically people, organizations, locations, and dates.
Semantic parsing: Semantic parsing is the process of determining the meaning of a sentence or phrase. This involves identifying the different concepts in the sentence, as well as the relationships between those concepts.
Coreference resolution: Coreference resolution is the process of identifying and resolving references to the same entity in a text. For example, in the sentence "John saw Mary and she smiled," the pronouns "she" and "Mary" refer to the same entity.
Sentiment analysis: Sentiment analysis is the process of determining the sentiment of a piece of text. This involves identifying the emotional tone of the text, such as positive, negative, or neutral.
These are just some of the key concepts in language processing in AI. Language processing is a complex and challenging field, but it is also a very rewarding one. Language processing techniques are used in a variety of applications, and they are likely to have an even greater impact on our lives in the years to come.
Here are some additional resources that you may find helpful:
Natural Language Processing with Python: https://www.nltk.org/book/ by Steven Bird, Ewan Klein, and Edward Loper
Speech and Language Processing: https://web.stanford.edu/~jurafsky/slp3/ by Dan Jurafsky and Christopher Manning
The Stanford NLP Group: https://nlp.stanford.edu/
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