Named Entity Recognition

The named entity recognition algorithm (NER) is an NLP method that processes unstructured data into different labels and tags. The only difference is that it assists search engines in producing better results that users wish to have. Furthermore, it even enables chatbots to respond in a more humanized manner. The NER is more useful in unstructured text. Thus, in the absence of database formatting, NER proves beneficial. 

There is an entire five-step process of NER. It starts with tokenization that slices text into smaller pieces for processing. These slices can be small or large sentences. The next step is using statistical models to identify these texts based on format or capitalization. Later, classification for the same and labeling into certain categories (person, location, or field) occurs. Furthermore, the NER systems utilize context clues and post-processing rules to produce more accurate results to label them into more subtle categories. 

Breaking them apart produces different types of NER models. Let us look at them in detail:

• Rule-based

This NER system relied on some instructions that allowed users to extract information (the names of objects) from text. However, rules must be created in either of two forms: pattern-based rules or context-based rules. While the former relies on a common sequence or pattern for word forms or texts, the latter is more concerned with the meaning or context of the word. 

• Dictionary-based

As the name suggests, the dictionary-based NER references items based on the alphabetical order of their names. However, it can be any collection of words related to a specific field or domain.

• Machine learning-based

Machine learning-based NER systems use statistical methods to identify an object’s name. This model must be built on annotated documents that incorporate past experiences to produce better results. 

• Hybrid systems

The hybrid systems are a combination of any of the above-explained NER systems. Its main goal is to strengthen the database and minimize the chances of risk as well.  

The named entity recognition model has a distinct and world-wide application in the finance domain. Due to the complexity created due to unstructured data, financial institutions face a huge challenge in managing and controlling such data. NER comes to the rescue by filtering data based on valuable patterns and detecting errors firsthand. One of the popular applications of NER is visible in Digit, a San Francisco company. It is an accounting services provider with an NER system enabled aiming to provide clients insights on transactions. 

Similarly, the personalization feature makes it possible to enable smarter search and find the accurate tag associated with it. Furthermore, NER also helps you monitor social media trends and extract entities and data from Twitter and Reddit forums. Nonetheless, it also helps you discover new products, content, and ideas in augmented search and power recommendations.

Let us dive into some examples to comprehend the concept in a better way:

Example #1

Suppose Johnson Waters Ltd is a hypothetical company engaged in the investment banking industry. They have helped many firms in gaining capital for their business. Since they deal with multiple clients and industries, the amount of unstructured data is also huge. One of such data included, “Firm A has announced financial results with a revenue of $5000 million for the fiscal year 2023-2024. The firm is also planning to have an acquisition of XYZ company for $200 million. The named entity recognition algorithm here segregated major objects under certain category labels. 

For example, Firm A and XYZ company come under the label “Companies.” Likewise, revenues and acquisition fall under “Financial metrics.” In the same manner, even $5000 million, $200 million, 2023-2024 can be categorized under “Monetary value.”

Example #2

According to a news article as of February 2023, Quantexa joined hands with Aylien, a popular named entity recognition project, to polish the world of structured and unstructured data into automated entities. This deal combines the advanced AI and Natural Language Processing (NLP) of Aylien with the Decision Intelligence Platform of Quantexa for tapping the $11bn text analytics market. Another Gartner report suggests that the unstructured data within their organizations increases by 30% year-over-year. 

NER systems have benefits to offer firms in the finance, healthcare, entertainment, and customer service sectors. If we delve deeper, there is more to offer. Let us look at them in brief:

• Better segregation of data

One of the prime benefits of NER systems is the processing of unstructured and structured data into entities. It identifies key data for future information retrieval. 

• Wide application

NER systems are widely applicable to most industries. Many popular firms and companies in all domains, like Google Cloud Platform, Amazon Web Services (AWS), and Microsoft Azure, have installed them in their database systems. 

• Eliminates the risk of errors

Due to minute filtration occurring in every stage, the risk of errors and missing out any data gets reduced to a major extent. It does not overlook but rather includes processes like post-processing to conduct the same. 

• Content organization

The data treated as entities are later labeled under certain categories for faster information retrieval. Also, it enables automated workflow to organize data better, thus saving time and resources. 

After looking at the advantages of named entity recognition models. Let us now look at the limitations of this system:

• In most cases, defining the rules and instructions for the NER system can also be hectic and complicated. 
• At times, updating NER systems is necessary to avoid lags and false-positive identifications. 
• When converting into text entities, there can be issues with the spelling and variation of words. 
• Sometimes, explaining machine learning-based NER outputs can be tough and challenging.