Natural Language Processing:
Natural Language Processing is a subfield of computer science and artificial intelligence that focuses on the interaction between computers and human language. It involves developing algorithms and techniques that enable computers to understand, interpret, and generate natural language.
NLP has many applications, including language translation, chatbots, sentiment analysis, and text classification. Some of the key components of NLP include:
Tokenization: Breaking down text into individual words or phrases, called tokens.
Part-of-Speech (POS) Tagging: Assigning grammatical labels, such as noun, verb, or adjective, to each token in a sentence.
Named Entity Recognition (NER): Identifying and classifying named entities such as people, organizations, and locations in text.
Sentiment Analysis: Analyzing the emotional tone of a piece of text to determine whether it is positive, negative, or neutral.
Text Classification: Assigning a category or label to a piece of text, based on its content.
Some of the techniques used in NLP include:
Rule-based methods: These methods involve creating a set of rules and patterns to identify and extract information from text.
Statistical methods: These methods involve training machine learning models on large datasets of labeled text, in order to identify patterns and relationships in the data.
Deep Learning methods: These methods involve training neural networks on large These methods involve training neural networks on large datasets of text, in order to learn and extract features from the data.