Named Entity Recognition, Natural Language Processing(NLP), types and overviews.

 

Named Entity Recognition (NER) is another sub arm of Natural Language Processing (NLP). In our previous post, we learnt that Sentiment Analysis is a subset of Natural Language Processing NLP, that is used in the application of algorithms.

Now talking about Named Entities Recognition(NER), they help to identify and classify specific objects, people, places, organizations, and other entities that have a name or title.

The objectives of Named Entity Recognition(NER) is to automatically identify and classify these named entities in text, and to extract relevant information about them. 

This identifications and classifications can be useful in a variety of applications, such as information retrieval, question answering, and text summarization.

Named Entity Recognition(NER), has several techniques used, including rule-based approaches, statistical models, and machine learning algorithms. These techniques rely on various features of the text, such as the presence of certain words or patterns, to identify and classify named entities.

In all these, NER is an important task in Natural Language Processing(NLP), as it can help improve the accuracy and efficiency of many applications that rely on understanding and extracting information from text.

As stated above, Named Entity Recognition (NER), as subset of Natural Language Processing (NLP), involves the act of identifying and classifying named entities in text. 

Now let us look at the types of named entities that can be recognized by NER systems:

1. Person: Refers to individuals, such as names of people, celebrities, or fictional characters.

2. Location: Refers to places, such as cities, countries, or landmarks.

3. Organization: Refers to companies, institutions, or other groups of people with a common purpose.

4. Time: Refers to dates, times, or durations.

5. Money: Refers to currency values or monetary amounts.

6. Percentage: Refers to percentages or ratios.

7. Miscellaneous: Refers to other types of named entities, such as product names, event names, or medical terms.

NER systems can be classified into three main categories:

1. Rule-based systems: These systems use a set of predefined rules to identify named entities based on patterns in the text. For example, a rule-based system might identify a named entity as a person if it appears in the text with a title such as "Mr." or "Ms."

2. Statistical models: These systems use statistical algorithms to identify named entities based on patterns in the text. For example, a statistical model might identify a named entity as a location if it appears frequently in the text with other location-related words.

3. Machine learning systems: These systems use machine learning algorithms to identify named entities based on patterns in the text. For example, a machine learning system might learn to identify a named entity as a person if it appears frequently in the text with other person-related words.

#NamedEntityRecognition

#ArtificialIntelligence

#AI