What are NERs?: Why Implementing Them is Crucial

Currently, there is much talk about Natural Language Processing (NLP). Linguistic technologies are gaining ground in this increasingly digital world, and their use is gradually expanding in professional sectors to automatically discover, classify, organize, or search for content. This can result in more efficient use of time, reduced expenses, and quicker decision-making in organizations.

One application of NLP is the Named-Entity Recognition (NER) tools, also known as entity recognition detectors. As the name suggests, NER detects entities such as people, locations, organizations, or brands. NER utilizes Machine Learning technology, rules, and linguistic corpora.

Named entity recognition serves to recognize specific entities regardless of the context. For example, we can identify a certain word as an organization, a city, a person, etc. NER algorithms use statistical models to understand words from a semantic and contextual perspective. Knowledge graphs further expand the relationship between entities and allow for a comprehensive understanding of data.

Perhaps in theory it may not seem so complex, but in practice, it might not be as easy for us. In these cases, if we want to apply this type of knowledge and are just getting into the world of programming, it is always recommended to hire a developer. Now, let's go back to NER. 

How does NER relate to NLP?

As mentioned earlier, natural language processing helps develop intelligent machines capable of extracting meaning from speech and text. Machine Learning aids these intelligent systems in continuous learning by training on large amounts of natural language datasets.

In general, NLP consists of three main categories:

• Syntax: Understanding the structure and rules of language.

• Semantics: Deducing the meaning of words, text, and speech, and identifying their relationships.

• Speech: Identifying and recognizing spoken words and transforming them into text.

NER contributes to the semantic aspect of NLP by extracting the meaning of words, identifying them, and locating them based on their relationships. NER activates various Machine Learning operations. Here are the most important ones:

Semantic Search

Semantic search is already available on Google. You can input a question, and it will do its best to respond with an answer. Digital assistants like Alexa, Siri, chatbots, and others employ a form of semantic search to find information users are looking for. This function may be unpredictable, but there is a growing number of uses for it, and its effectiveness is rapidly increasing.

Data Analytics

This is a general phrase for using algorithms to create analysis from unstructured data. It integrates methods to display this data with the process of searching and collecting relevant data.

This could take the form of a direct statistical explanation of results or a visual representation of data. Analyzing interest and engagement in a particular topic can be done using information from YouTube views, even when viewers click on a specific video.

Sentiment Analysis

Delving deeper into NER, sentiment analysis can distinguish between good and bad reviews even in the absence of star rating information. It is aware that terms like "overrated," "complicated," and "stupid" have negative connotations, while terms like "useful" and "fast" have positive ones. Sophisticated algorithms can also recognize relationships between things.

Text Analysis

Like data analysis, text analysis extracts information from unstructured text strings and uses NER to focus on important data. It can be used to gather data on mentions of a product, average price, or terms customers most frequently use to describe a particular brand.

Video Content Analysis

More complex systems extract data from video information through facial recognition, audio analysis, and image recognition. With video content analysis, you can find YouTube unboxing videos, Twitch game demonstrations, lip-syncs of your audio material in Reels, and more.

To avoid missing important information about how people connect to your product or service as the volume of online video material grows, faster and more ingenious techniques for NER-based video content analysis are essential.

Different NER Approaches

The following three approaches are generally used. However, you can also choose to combine one or more methods.

Dictionary-based Systems

The dictionary-based system is perhaps the simplest and most fundamental NER approach. It uses a dictionary with many words, synonyms, and a vocabulary collection. The system checks if a particular entity present in the text is also available in the vocabulary. Using a string-matching algorithm, a cross-check of entities is performed.

A drawback of using this approach is the constant need to update the vocabulary dataset for the effective functioning of the NER model.

Rule-based Systems

In this approach, information is extracted based on a set of predefined rules. Two main sets of rules are used: pattern-based rules which follow a morphological pattern or chain of words used in the document and context-based rules which depend on the meaning or context of the word in the document.

Machine Learning-based Systems

In machine learning-based systems, statistical modeling is used to detect entities. This approach uses a feature-based representation of the text document. It can overcome several drawbacks of the first two approaches as the model can recognize entity types despite slight variations in spelling.

Most NER tools in the market have limitations regarding the number of languages or topics they analyze, as achieving both good precision (correctly identifying terms while avoiding false positives) and coverage (extracting most terms while avoiding false negatives) in many languages and topics is costly.

Choosing the best alternative will depend on your time, finances, and skill set. For any type of business, entity extraction and more sophisticated text analysis technologies can be advantageous.

When machine learning tools are trained correctly, they are accurate and do not overlook any data, saving time and money. Additionally, you can set up these solutions to run continuously and automatically through API integration.

A final piece of advice: when using a NER tool, knowing the topic and language the engines have been trained on can give you an idea of the tool's accuracy and coverage. If you're unsure how to access this information, it never hurts to seek out an expert who can guide you.