Natural Language Processing (NLP) involves a range of techniques and components aimed at understanding, interpreting, and generating human language data. The main components of NLP include:
Text Preprocessing: This involves cleaning and preparing text data for analysis. It includes tasks such as tokenization (splitting text into words or sentences), lowercasing, removing punctuation, stop word removal (removing common words like "the", "and", "is"), stemming or lemmatization (reducing words to their root forms), and handling special characters or symbols.
Tokenization: Tokenization involves breaking down a piece of text into smaller units, such as words, phrases, or symbols, known as tokens. This step is crucial for further analysis and processing of text data.
Text Representation: Text data needs to be converted into a numerical format for machine learning algorithms to process it. Common techniques for text representation include bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and word embeddings (such as Word2Vec, GloVe, or FastText).
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AI Training in PuneNamed Entity Recognition (NER): NER is a subtask of information extraction that aims to identify and classify named entities (such as person names, organization names, locations, dates, etc.) mentioned in text.
Part-of-Speech Tagging (POS Tagging): POS tagging involves labeling each word in a sentence with its corresponding part of speech (such as noun, verb, adjective, etc.). This information is useful for understanding the grammatical structure of sentences.
Syntax and Parsing: Syntax analysis involves parsing sentences to analyze their grammatical structure and relationships between words. Dependency parsing and constituency parsing are common techniques used for this purpose.
Sentiment Analysis: Sentiment analysis aims to determine the sentiment or opinion expressed in a piece of text, whether it is positive, negative, or neutral. It is often used for analyzing customer reviews, social media sentiment, and feedback data.
