Natural Language Understanding (NLU) is a branch of artificial intelligence that focuses on making machines understand human language. You can learn about various techniques and algorithms used in NLU to achieve this goal. I'll try to explain it to you as simply as possible.
1.
Tokenization: This technique involves breaking down a sentence into smaller parts or tokens, such as individual words or phrases. Tokenization helps the machine understand the structure of the sentence and identify the main components.
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Part-of-speech tagging: Once a sentence has been tokenized, part-of-speech (POS) tagging can be applied to each token. POS tagging labels each word in a sentence according to its grammatical role, such as noun, verb, adjective, adverb, etc. This helps the machine identify the relationships between the different parts of the sentence.
3.
Named entity recognition: This algorithm identifies specific entities within a sentence, like people, places, organizations, dates, and other important data. It's important because understanding who or what is being referred to can help with tasks like sentiment analysis or language translation.
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Sentiment analysis: This technique involves analyzing the emotion or tone of a sentence. Sentiment analysis can help determine whether a sentence is positive, negative, or neutral, which is useful for tasks like social media monitoring or customer feedback analysis.
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Dependency parsing: This algorithm analyzes the grammatical structure of a sentence by identifying the relationships between its different parts. It can help the machine understand the meaning behind the words and the context of the sentence.
These are just a few of the many techniques and algorithms used in natural language understanding and I hope that answers your question.
