Named Entity Extraction also known as entity recognition – is a natural language processing (NLP) technique that identifies and extracts named entities from any given text and classifies them into predefined categories.
These named entities can be organizations, people, locations, events, monetary values, quantities, and even expressions of time. In layman’s terms, it extracts all known entities, physical and abstract.
What is an entity?
Any solitary, recognizable, and distinct thing can be referred to as an entity. Individuals, organizations, systems, chunks of data, and discrete system components are referred to as “important in and of themselves.”
The common denominator of an entity is that it can be considered a separate whole with its own unique set of characteristics. Here are some examples of entities in different contexts:
- General computing: It usually refers to users, components, and organizations.
- System: It refers to discrete and separate components
- Database system: Individual things, such as individuals, concepts, or objects, with data stored in a database management system (DBMS) with characteristics and relationships to other entities.
- Object-oriented programming: It refers to synonymous with objects.
- Open Systems Interconnection model: Describes distinct system components that communicate with one another via distinct protocols.
How does Named Entity Extraction Work?
When you read any given piece of text, you can easily recognize entities like individuals, locations, values, and so on. Let’s look at an example, “Twitter still stands on its decision to ban the United States former President Donald Trump”. In this sentence, we can identify three entities,
- Organization: Twitter
- Location: United States
- Individual: Donald Trump
While you can easily identify and categorize these entities with ease, the same cannot be said for computer systems. They need natural language processing (NLP) and machine learning to make sense of human language. While NLP helps in understanding human language, machine learning techniques contribute to analyzing, categorizing, and increasing the accuracy of the analyzed data over time.
To understand what an entity is, the entity extraction model must first be able to identify words or a string of words that form an entity. Then it must be able to categorize them accordingly. For example: Entity: United States, Category: Location.
To identify entities like people, location, organizations, and such, the entity extraction model must first be trained with enough data. You have to update the sample of data with corresponding entities to teach the model. You then add more data over time to increase the accuracy of your entity extraction model.
What are the Applications of Named Entity Extraction?
Now that you have a better understanding of what a named entity extraction is and how it works, let’s check out some of its applications as well.
Text analysis has a wide range of applications ranging from enhancing browsing experience, automatizing CRM tasks, and even developing an emergency response mechanism. But if the algorithm starts analyzing and extracting each word in large datasets, the process will become too tedious and time-consuming. Furthermore, allocating hardware resources to speed up the process would require substantial financial resources.
Hence, rather than classifying each word, named entity extraction can scan documents to classify the most crucial elements. It can analyze text data sources such as documents, newsletters, online news publications, and more to identify entities like people, location, organization, and monetary values. This can help you categorize related information. You can then choose any group from the categorized data for further analysis.
Categorizing customer support tickets
Major brands and businesses have to go through a ton of customer support tickets regularly. Manually analyzing each customer query can take a substantial amount of time which can increase the response time and diminish the customer support experience for the customers.
Named entity extraction can help you categorize these customer support tickets based on the query. You can then allocate it to the right customer support executive. This helps you decrease the initial response time and enhance the overall customer support experience.
Many modern applications and e-commerce websites rely on recommendation systems to enhance the overall user experience. A great example of this is the widely used video streaming platforms such as Netflix and YouTube. They use named entity recognition to analyze you search history and recommend suggestion based on them.
For Example, if you search comedy movies on Netflix, it will analyze it with named entity recognition and recommend more movies from the same category for you.
Extract insights from customer feedback
Online reviews on various platforms are a great source of customer feedback. They can help you identify what customers like and what they don’t. Analyzing these reviews can help you identify the positives and negatives about your brand, product, or service and as well as the areas that need improvement.
Named entity extraction can help you categorize customer feedback and identify recurring issues. For example, you can identify locations that receive the most customer complaints, similarly, you can also identify products or services that draw the most customer support tickets.
Finding a capable candidate is not an easy task, recruiters have to manually go through a lot of resumes and analyze their qualifications, skills, experience, and more. This can take a substantial amount of time thus making it a lengthy and tedious process. But what if you could simplify this process by automating the analysis of resumes to find the most eligible candidate to interview.
Named entity extract can help you achieve that by analyzing the text in tons of resumes to find the most eligible candidates.
Semantic annotation can be defined as the process of combining various pieces of information to concepts such as people, places, and things. Unlike typical annotations, semantic annotations can help machines interpret human language. Semantic annotation involves text identification & analysis, concept extraction, relationship extraction, and indexing, Named entity extraction is the part of semantic annotation and helps analyze the data.
How to Perform Named Entity Extraction?
The best way to perform named entity extraction is by using an API. There are two types of APIs you can choose from.
- Open-Source Named Entity Extraction APIs
- SaaS Named Entity Extraction APIs
Open-Source Named Entity Extraction APIs
Open-source APIs can be used by developers, they are free to use and flexible but involve a bit of a learning curve to build an entity extraction model.
- SpaCy: A Python-based framework known to be quick and easy to use. It comes with a powerful statistical system that you can use to create custom NER extractors.
- Natural Language Toolkit (NLKT): The Python library suite is used extensively for NLP tasks. NLKT is equipped with a separate classification model that recognizes named entities named ne chunk but also has the Python wrapper to use the Stanford NER tag.
- Stanford Named Entity Recognizer (SNER): Stanford Named Entity Recognizer (SNER): This named entity extraction is a JAVA tool developed by Stanford University. It offers pre-trained models to extract entities and is based on Conditional Random Fields (CRF).
SaaS Named Entity Extraction APIs
SaaS tools are fully developed and ready-to-use solutions that you can use to build your custom named entity extraction model.
- BytesView: BytesView is a SaaS-based text analysis solution that offers various models to analyze large volumes of textual data. The analysis models are ready to use and do not require highly technical skills similar to developers. The analysis models also include named entity extraction.
- MonkeyLearn: MonkeyLearn is a widely known SaaS-based text solution that can help you analyze any piece of textual data with their various analysis models including named entity extraction.
- Lexalytics: Lexalytics is another widely popular SaaS-based text analysis solution that offers various pre-trained analysis models for analyzing textual data including named entity extraction.
How to Build your custom Entity Extraction Model with BytesView?
To train your custom entity extraction model using BytesView you have to utilize textual data related to your business. By training your custom model you can increase the accuracy of the analysis model.
To train your own custom model, just follow these steps,
- Collect and export the information in a CSV or Excel file to train the model. Use a web scraping tool or let us do it for you.
- Select a classifier or extracting model and click “Create a model” on the dashboard.
- Click on the extractor and select the entity extraction model.
- Import your data and select the column you want to analyze if more than one is available.
- Tag text relevant text to train the entity extraction model. After a few tags, the model will start drawing its conclusions.
- Test your model to see how accurately it works.
- Once the model is trained with data, you can start extracting entities from unstructured text.
Using entity extraction APIs is by far the most popular way of utilizing named entity extraction. Although, which API to choose depends solely on your skillset, resources, and time. You can choose to build your model using open-source APIs if you have the required skills. If not, you can choose a SaaS-based solution to get started.
With BytesView however, you do not require any kind of specialized skills to build your custom model. Do give it a try!