What Are Named Entities And How Can Named Entity Recognition Be Used

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Named Entity Recognition involves automatically scanning unstructured text to find “entities”, standardizing and classifying terms, for example, by the names of individuals, organizations (such as companies, government organizations, committees). These entities, known as named entities, refer to terms that represent real-world objects, such as the names of people, organizations, places, times, quantities, monetary values, percentages, etc. They are usually referred to by proper names. A naive way may be to find them by searching sentences in text documents.    

Named Entity Recognition (NER) is an information extraction subtask that aims to find and classify atomic elements, quantities, etc. in text based on predefined categories such as names, organizations, locations, and time expressions D. Currency value, interest, etc. It works with high precision to identify common objects such as names, location, organization, etc. You can extract key information to understand what is at stake through named recognition entities or simply use it to gather important information to store in a database. Companies can use Named Entity Recognition (NER) to tag relevant data on customer service tickets, detect entities mentioned in customer reviews, and easily extract critical information such as contact information, location, dates, among others.    

Use Cases

An entity can be any word or series of words that always refer to the same entity.    Unstructured text content is full of information, but finding what you need is not always easy. Extracting basic entities from text helps us organize unstructured data and discover important information, which is very important for processing large data sets.    

For example, we can also use entity extraction to extract related information such as product name or serial number, it is easier to route the ticket to the most appropriate agent or group to solve such problems. You can also use entity extraction to extract relevant data, such as product names or serial numbers, making it easier to send work orders to the best agent or team to solve the problem. Using the entity extractor, the recruitment team can immediately extract the most relevant information about the candidate, from personal information (such as name, address, phone number, date of birth, and email address) to their education and experience (such as certificates) . , Education, company name, skills, etc.). Then, using the entity extractor, the recruitment team can immediately extract the most important information of the candidate from personal information such as name, address, phone number, date of birth, and email address.

Basically, the ultimate goal of NER systems is to extract meaningful information about objects that appear in raw data, as in a word processing document. As stated, NER helps us identify and extract important elements from text data such as names of people, geographic location, organization, monetary values, etc.    

How it works?

Using statistical modeling, the NER system can accurately classify objects. In addition, the information extracted from these NER (Named Object Recognition) models can be used to retrain the same algorithm to achieve higher accuracy. Entity mining can provide useful information about unknown data sets, at least immediately revealing the objects and content of the information. The dictionary-based approach uses a large database of named entities and may run terms from different categories as references for finding and marking entities in a specific text.    

We then rely on natural language processing (NLP) techniques like Named Entity Recognition (NER) to identify and extract underlying entities from any text document. While it is important for NER and RD to be a prerequisite for many machine learning operations based on text mining, Entity Mining technologies face several linguistic challenges associated with correctly identifying and classifying entities.   

Challenges 

Although it is easy for a person to distinguish between different types of names (like person, place, organization, product, etc.), as rigid designations usually include proper names and some natural terms such as species and biological substances.    

In addition to its powerful Sentiment Analysis tool, Kommon Poll provides a complex Named Entity Recognition tool that extracts key names and relationships contained within individual mentions for a given keyword and finds the most important relationships between the keyword searched.

Try out Kommon Poll now with a free trial!

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