Named Entity Recognition (NER), sometimes referred to as entity clustering, extraction, or identification, is the task of identifying and categorizing important information (entities) in texts. But, what are these things we call entities?
An entity can be any word or sequence of words that consistently refers to the same thing. Entities are the most critical parts of a particular sentence, such as noun phrases, verb phrases, or both. Each detected asset is classified into a predetermined category. For instance, a NER machine learning (ML) model might find the word "Microsoft" in a text and classify it as "company."
NER is a form of natural language processing (NLP), a subfield of artificial intelligence.
NLP is about computers that process and analyze natural language, i.e., any language that evolves organically, not artificially like computer coding languages. If you’d like to learn more about NLP, you can check out our article, "What is Natural Language Processing and How Can It Be Applied?" Now, let's take a closer look at the implementation of Named Entity Recognition.
Simply put, as observers, we can recognize named entities like people, values, locations, and so on after reading a particular text.
Let’s look at the following text for examples:
As we can see in the text above, it is possible to define named entities such as persons, organizations, and locations. However, for computers to do the same—that is, to categorize them— we first need to help them identify entities. To do this, we need the help of Machine Learning and Natural Language Processing (NLP), one of the most important and widely used text analysis applications. While applying NER using computers, NLP examines the structures and rules of language to create intelligent systems that can extract meaning from text and speech. And finally, Machine Learning helps computers acquire knowledge of the language in order to evolve over time. To find out what an entity is, a NER model needs to be able to detect a word or string of words (for example, State of California) that makes up an entity and decide which entity category it belongs to. Ultimately, we can say that at the core of any NER model is a two-step process:
Therefore, we first need to create the entity categories such as name, location, event, and organization and then feed the training data related to the NER model.
If you are wondering how to develop NER (Named Entity Recognition) applications or text analytics softwares, you can use the open-source NLTK, SpaCy, and Stanford NER libraries that are widely used in this field. It is also possible to discover the pros and cons of each library by clicking on the links. Additionally, a list of the named entity types we may encounter in these libraries is in the table below.
NER can be applied to any situation where a high-level overview of large amounts of text is useful. With NER, we can understand the subject or theme of a text at a glance and quickly group written contents according to their relevance or similarity. Let's look at some common uses and cases together to understand how NER makes your work easier.
Using text analysis softwares and text analysis techniques, you can transform unstructured data to insights. In some cases, conversational data (speech analytics) is also transformed into text data and unstructured text data is applied NER to quickly understand what is being mentioned.
Widely used phrases and names are extracted to allow the news related to relevant people, places, industries, and institutions more accessible. In the case of news content, NER is used for the acquisition of knowledge about relevant news tags, automatic categorization in defined hierarchies, and content discovery.
Text classification can save your time!
Say that you're working for an NGO and you're trying to understand what people think about refugees. You can transform the data via NER to understand which countries people mention most, which politicians they are talking about and which organizations they are discussing among those conversations.
A NER model can easily identify relevant customer complaints, inquiries, and feedback based on key information such as product names, specifications, branch location, and more. Complaints or feedback are appropriately classified and forwarded to the right department by filtering priority keywords. Thus, the time it takes for the problem to be transferred to the relevant unit is considerably shortened. If analyses are conducted with social listening tools over social media platforms like Twitter, the information regarding what kind of problem is occurring in which product or location is automatically determined. A company can then go on to make accurate investments in given areas by conducting the necessary analyses with the data it has obtained.
Say that you're working for an enterprise company who sells automobiles to people. You can analyze customer conversations that is forwarded from your contact center and turn them into insights to understand which parts of your car is more problematic by using Named Entity Recognition.
One of the things to be considered when translating between different languages spoken by people is that linguistic information such as proper names remains unchanged in the translation system. For instance, in the English language, the word “daisy” is both a common noun, meaning “the flower” and a proper noun, often used as a female human name.
Let’s consider the sentence, “Daisy is going to stop by to our office tomorrow.” A native speaker would naturally understand that in this specific instance, the word “Daisy” is a proper noun referring to a person. However, a translation considering “Daisy” a common noun would declare that it will rain today near the speaker. Most languages have names and phrases that can be lost in translation which is why the extraction of assets is vital for translation systems.
You may have encountered various tools that scan resumes and extract essential information from them, such as name, address, and qualifications. Such tools often make use of NER (Names Entity Recognition) to help pull this information.
One of the most challenging tasks facing HR departments across companies is evaluating a massive pile of resumes to shortlist candidates. Many of these CVs are extremely detailed, and much of the information is irrelevant to those who assess them. By utilizing NER, relevant information can be easily extracted for the assessors. This implementation reduces the time and effort human resources professionals spend on shortlisting candidates from numerous resumes.
Today, many platforms such as Netflix and YouTube use recommendation systems to produce optimal customer experiences. Most of these systems rely on NER, which can make recommendations based on user search history. For example, if you watch a lot of educational videos on YouTube, you'll get more content suggestions that are classified as educational assets.
For almost any product and service-based company, online reviews are a great source of customer feedback. Online reviews can provide a wealth of knowledge about what customers like and dislike about your products and services as well as information regarding aspects of your business that need improvement for business growth.
So NER can be employed to organize all customer feedback and detect recurring issues. For example, it is possible to use NER to identify locations most frequently cited in negative customer feedback, which might help direct focus on a particular office branch.
At this point, Kimola Cognitive, our no-coding machine learning platform that analyzes consumer conversations with its technology, comes into play. We're talking about an enormous amount of consumer feedback data that can't be handled with human power. Kimola's Cognitive product plays an important role in analyzing this vast amount of consumer data. Kimola Cognitive, a web-based ML platform without any prerequisite coding knowledge, enables us to benefit from Machine Learning as well as Natural Language Processing technologies via its Unsupervised Named Entity Recognition feature.
With machine learning, it becomes possible to determine the categories of consumer opinions according to each industry. Plus, thanks to NER, textual contents are tagged in 10 different asset categories (GPE, ORG, Interests, Human, NORP, etc.) determined by Kimola, enabling the extraction of invaluable insights.
If you have an NLP data set, you can create a free account at Kimola Cognitive, determine which categories the content in your data set is concentrated in, capture the assets in the content, and get insights that will enable you to take important actions for your brand.
🤖 Also see: How to analyze data with Text Analysis feature?
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