Businesses and organizations deal with large numbers of electronic documents daily. Sifting through all of this information can be time-consuming and difficult. Named entity recognition (NER) is a form of natural language processing (NLP) that can help businesses and organizations manage this information faster and more efficiently.
A High-Level Introduction to Named Entity Recognition
Named entity recognition algorithms use sentences or paragraphs as inputs. Their task is to search text and find specific names, businesses, organizations, or other objects in the text. NER then sorts entities into predefined categories.
In general, if you want to get to the bottom of the topics covered in a large volume of text files, NER can make the process easier.
What Is a Named Entity?
Although the technology is often associated with processes that search text for proper names, locations, organizations, and numbers, named entities don’t have to be proper nouns or numbers. They can be any object or idea that can be represented by text.
How is NER Used?
Named entity recognition is well-suited to use cases that present the problem of prescreening, sorting, or searching large numbers of text files. Some of the industries that have implemented NER to make documents and text more accessible include:
One of the sectors driving demand for named entity recognition is healthcare. NER can find disease names, medications, laboratory tests, and payers in documentation, sort those files and share that data with systems that alert people who need it to perform their jobs effectively.
Customer support requests can vary widely. Consumer communications can focus on positive or negative feedback, service requests, or simply ask a question, such as where they can buy a manufacturer’s products. NER can sort those customer support inquiries and route them to the right departments for faster responses.
Searching for documents for a specific person or business manually is time-consuming. Named entity recognition can search millions of documents, records, newspaper articles, or other text data sources in a fraction of the time it would take a human to find text that contains specific entities.
This use of NER can help an online publisher make recommendations for related articles when a user clicks on a title or topic. It can also help a business monitor its online reviews or an e-commerce retailer search for a consumer’s records for deletion under the EU General Data Protection Regulation (GDPR) “right to be forgotten.” In each case, NER brings speed and accuracy to the task.
Data scientists are tasked with extracting value from the vast data stores that companies have acquired. However, when a business manager requests analysis to guide product development, marketing or sales, data scientists need to determine which datasets contain information that will help provide relevant, accurate insights.
However, writing SQL queries to find suitable datasets for analysis can be extremely time-consuming and could delay critical answers a business needs. NER can help data scientists search databases more quickly and spend more time on higher-level tasks.
Research and Academia
Corporate R&D teams and university researchers can benefit from work others have performed in their fields –– if they can find it. NER helps researchers quickly find relevant reports, theses, and papers. Furthermore, NER can classify and organize the information into subcategories to save time and enable a data-driven research approach.
Competition for top talent has become fierce as more people make career changes. The Great Resignation continues, with 4.2 million U.S. workers leaving their jobs in June 2022. As a result, HR teams’ workloads have increased. They must review applications and resumes to prescreen applicants, review job applications, and compile records on job candidates to review before the organization makes hiring decisions. NER can streamline that process, looking for previous work experience, education, and skills applicants need.
What Are the Major Approaches to NER?
Depending on the use case, solution builders can take different approaches to NER. They can use one or any combination of these methods:
As the name implies, this type of named entity recognition uses a dictionary for its vocabulary. Named entity recognition algorithms check entities in the text against words in the dictionary. One of the drawbacks of this method is that vocabulary expands and changes, which would require constant updates for the algorithm to work correctly.
This approach uses rules from patterns of speech rather than merely dictionary definitions. Pattern-based rules are based on how words are used together in speech, and context-based rules look at how a word is used within the document.
NER based on machine learning, is a statistical model that makes a representation of data. Using machine learning, a platform can recognize entities even if they aren’t spelled correctly or if the syntax is unusual.
To take the machine learning-based approach, solution builders must train the model with annotated data. Then, the model is tested to see if it can appropriately annotate raw data. When the model operates within performance quality parameters, it’s deployed for real-world use.
What are Top NER Challenges?
AI project teams have been able to create NER platforms that perform exceptionally well. However, teams have challenges to overcome.
The first is developing quality datasets to train the named entity recognition algorithm. NER solutions are most effective when they’re trained specifically for their domains. Collecting enough data and finding the time, resources and expertise to annotate it properly can be a hurdle to project completion.
Another challenge is enabling recall and precision. “Recall,” is its ability to find each instance of an entity within a dataset. If NER finds it 10 times, but it was actually present 100 times, recall is 10%. A number much closer to 100% is preferable. Likewise, if the NER engine finds 100 instances, but they are all inaccurate, its precision is 0%. The NER project team must train the algorithm to deliver good results for each metric.
What’s the Link Between NER and NLP?
Named entity recognition is a form of natural language processing (NLP), and, in most cases, they work together. NLP converts human language into formats that machines can use to understand what a writer or text creator is communicating. NER identifies entities in the text, NLP helps the platform interpret and understand them, and then NER classifies the data.
NER and NLP also work together to enable named entity recognition disambiguation (NERD), aka named entity disambiguation (NED), which allows users to assign unique identities to some entities to avoid irrelevant or inaccurate results. For example, “Lincoln” can refer to a president, a place, a name, or an automobile. With NERD, how the word is used in the text will determine how that data file is classified.
Conversely, people may refer to the same thing with different names. One may type “The XYZ Company,” another may type “XYX, Inc., “and another may type only “XYZ.” Named entity normalization enables a machine to recognize the writer’s intent and classify all of those text files in the same way.
How will NER Be Used in the Future?
As NER technology advances and solution builders enable machines to identify named entities with greater precision and disambiguation, NER can take on more tasks in vertical markets currently using this technology and expand to new industries.
NER may also allow businesses, educational institutions, nonprofit groups, political organizations and more to tap into the vast stores of data locked in documents. This technology will enable technology to search them by the millions to find information about specific named entities.
How Can You Implement Named Entity Recognition?
Numerous tools are available to help you implement NER for your project, including:
- spaCY: an open-source NLP library that includes built-in methods for NER
- Stanford NER tagger: a Java implementation, also known as CRFClassifier.
- Tools for the CLARIN infrastructure
- BERT in PyTorch
Also, search for tools specifically for your domain to take your solution to market faster.
Conclusion: A Successful NER Program Depends on How You Annotate Data
ML-based algorithms and models designed for NER must be trained properly for your use case. Machine learning platforms have vast potential to solve problems and automate tasks for your organization. However, one thing they can’t do is work correctly without training. NER projects require large volumes of quality training data to learn how to identify and classify the named entities in the text data your organization needs to operate and make informed decisions.
Sigma’s team of skilled data annotators has the knowledge and experience to develop quality training and testing datasets for your project. We partner with you from start to finish to ensure a successful project, from planning effective data annotation strategies to evaluating results, adapting processes, and scaling as your project’s needs change.
Sigma also has AI-assisted tools for optimal efficiency and project managers with expertise in NER and NLP to help you address issues and keep your project on track.
To take your next step toward successfully annotating datasets for named entity recognition projects, start here!