Named Entity Recognition (NER) is a method of extraction that is used to identify and classify named entities in a given text. This can be done through the use of pre-trained models or custom-trained models. NER can be used for a variety of tasks such as entity classification, information extraction , and question answering. Some examples of named entities include persons, locations, organizations, products, and events.
How to do NER
To perform NER, we first need to tokenize the text into sentences and then tokenize each sentence into words. Then, we can use a pre-trained model or train our own custom model to identify and classify the named entities in the text.
There are many different ways to perform NER, but one of the most common methods is using support vector machines (SVMs). SVMs are a type of machine learning algorithm that can be used for both classification and regression. In the case of NER, we use SVMs to classify named entities into predefined categories, such as person, location, or organization.
Once the text has been classified, we can then extract information about the entities. For example, if we are looking for persons, we can extract their names, titles, and affiliations. If we are looking for locations, we can extract the latitude and longitude coordinates. And if we are looking for organizations, we can extract the name of the organization and the type of organization.
NER is a very useful tool for a variety of tasks such as information extraction, entity classification, and question answering. It can be used to extract a wide range of information about named entities in a text.
Some of the Useful Information for NER
- financial amount
- telephone number
- e-mail address
- license plate number
- social security number
NER in Data Science
In data science, NER is often used for information extraction. For example, imagine we have a dataset of customer reviews and we want to extract information about the product they are reviewing. We can use NER to identify all of the named entities in the text (e.g., product names, brand names, etc.), and then extract information about those entities.
NER can also be used for entity classification. For example, imagine we have a dataset of news articles and we want to classify each article by the type of named entity it is about. We can use NER to identify all of the named entities in the text (e.g., persons, locations, organizations, etc.), and then classify each article accordingly.
Finally, NER can also be used for question answering. For example, imagine we have a dataset of FAQs and we want to extract the answer to a specific question. We can use NER to identify all of the named entities in the question (e.g., person, location, organization, etc.), and then extract the answer from the text.