Named Entity Recognition (NER)

Part-of-speech tagging (POS tagging): The process of assigning a grammatical category (such as noun, verb, adjective, etc.) to each word in a sentence.

Named Entity Recognition (NER) algorithms: The methods and techniques used to identify and extract named entities from text.

Entity types: The various categories of named entities that can be recognized, such as persons, organizations, locations, etc.

Feature engineering: The process of selecting and transforming the input features (such as the context of a word) that are used in NER algorithms.

Machine learning techniques: The statistical and computational methods used to train NER models to recognize named entities.

Supervised learning: The type of machine learning approach where a model is trained on labeled data (i.e., data where the named entities have already been identified).

Unsupervised learning: The type of machine learning approach where a model tries to discover patterns in the data on its own (i.e., without labeled data).

Semi-supervised learning: The type of machine learning approach where a model is trained on a small amount of labeled data and a large amount of unlabeled data.

Neural networks: A type of machine learning model that is particularly well-suited for natural language processing tasks, including NER.

Evaluation metrics: The measures used to determine how well a model is performing when identifying named entities. The most common evaluation metric is F1-score.

Person: Identifying names of people, including their first and last names, titles, and honorifics.

Location: Identifying names of places, including cities, countries, or other geographical locations.

Organization: Identifying names of companies, institutions, or other organizations.

Date and Time: Identifying date and time expressions in text, including specific dates, intervals, or relative time expressions.

Money: Identifying numeric expressions that represent money, including currency codes and units of measurement.

Percentage: Identifying numeric expressions that represent percentages.

Product: Identifying names of products or goods, including brand names or models.

Event: Identifying names of events, including sports games, music concerts, or festivals.

Law: Identifying names of legal terms, including laws, regulations, or contracts.

Religion: Identifying names of religions or religious figures.

Nationality: Identifying names of nationalities or ethnic groups.

Language: Identifying names of languages or dialects.

Health: Identifying names of diseases, medications, or medical procedures.

Science: Identifying names of scientific concepts or phenomena.

Sport: Identifying names of sports or athletes.

Misc: Identifying other named entities that do not fall into any of the above categories.