Named Entity Recognition

Part-of-speech tagging (POS): This involves assigning grammatical tags (noun, verb, adjective, etc.) to every word in a sentence.

Chunking: This involves grouping together words into chunks based on their part of speech, usually using regular expressions.

Dependency parsing: This is the process of assigning a syntactic structure to a sentence, showing the relationships between words.

Entity recognition: This is the process of identifying entities such as people, organizations, locations, products, etc. in a text.

Named entity recognition (NER): This is a specific task within entity recognition that involves identifying and classifying named entities.

Training and evaluation: This involves building and testing NER models using annotated data.

Supervised learning: This is a machine learning approach where models are trained on labeled data.

Unsupervised learning: This is a machine learning approach where models are trained on unlabeled data.

Rule-based methods: These involve creating rules based on linguistic patterns to identify named entities.

Probabilistic methods: These involve using statistical models to predict named entities.

Deep learning methods: These involve neural networks that learn representations of text and use them to identify named entities.

Feature engineering: This involves selecting and extracting features from text data to use as input to machine learning models.

Evaluation metrics: These are the measures used to assess the performance of an NER model, such as precision, recall, and F1 score.

State-of-the-art approaches: These are the most advanced NER methods being used in industry and research today, such as transformer-based models.

Application areas: These are the different domains that NER is used in, such as information extraction, sentiment analysis, and chatbot development.

Person: Names of people such as "John" or "Mary".

Location: Names of places such as "New York City".

Organization: Names of companies such as "Microsoft" or "IBM".

Product: Names of products such as "iPhone" or "Coca-Cola".

Event: Names of events such as "Super Bowl" or "Olympics".

Date: Descriptions of dates such as "January 1st, 2021".

Time: Descriptions of time such as "5:30 PM".

Money: Descriptions of money such as "$5.00" or "2 million dollars".

Percentage: Expressions of percentages such as "50%" or "25.5%".

Miscellaneous: Other types of entities such as "email", "URL", "phone number", etc.