Natural Language Processing (NLP) is a powerful tool that can help businesses derive value from large volumes of text. NLP is the branch of machine learning (ML) that focuses on training computers to understand written language, a skill that comes naturally to humans and has historically been very difficult for machines. Many businesses have natural language at the center of their workflows. Whether the task is reading news articles, sorting legal documents, finding patterns in call transcripts, or understanding written reports, most companies spend a lot of time working with text. In this article, we will discuss a few ways to identify when NLP can be used to make these natural language workflows faster and more efficient.
Scale
Scale is the first point to consider in determining NLP applications within your business. If a single employee spends an hour each day sorting documents into categories, an ML model may be able to help them do this job more quickly, but the cost of purchasing or building and maintaining a model for such a small use case far outweighs the benefit.
ML solutions provide the most value when they are run at scale. If hundreds of employees are spending hours a day classifying documents, then the scale is large enough for an NLP model to be considered. NLP is being used to help analysts understand trends in high-volume, high-velocity data landscapes such as news streams, cybersecurity logs, and social media feeds. These are cases where the scale of the natural language text is so large that it would require vast amounts of person-hours to process. NLP models can make dealing with this data tractable and cost-efficient.
Problem Type
NLP is best-suited to solve discrete problems. A discrete problem has a clear input and a clear output, as well as a definite “right answer.” Often, workflows involving natural language must be broken down into several discrete tasks in order to apply NLP.
For example, consider the task of understanding market signals from news feeds. A human working on this task would likely perform many smaller subtasks, often without thinking about them as discrete problems, such as finding relevant articles, identifying the companies in each article, pulling out financial metrics, and plotting trends. While a single model cannot automate this whole process, we can use ML at each step: a text classifier can identify articles about financial markets, a named entity recognition model can pick out companies and numbers, and a linear regression model can find patterns in the data over time. By breaking down complex workflows into their discrete tasks, we can apply NLP in the places where it can deliver the most value.
Accuracy
Accuracy is the third and final point to consider when determining if NLP is a good fit for your business. While modern advances in machine learning research have led to models that can, at times, match human performance on specific tasks, even the industry’s best NLP solutions are imperfect and should be used with care.
The predictions offered by ML models are probabilistic – while a model may be confident in its answer, there is always a chance that it is incorrect. This means that NLP solutions should be used in situations where imperfect accuracy is acceptable.
For example, if your model is being used to predict the sentiment of social media posts about your company, it is likely making thousands of predictions per day; if a few of these predictions are incorrect, those errors will have very little impact on the aggregate result. However, if you are using an NLP model to rate college admissions essays, a single incorrect prediction from the model could have a large negative impact on an individual. When using any machine learning solution, we must expect errors and work to mitigate their impact.
Natural Language Processing is a powerful tool that can be used to improve the efficiency of many business processes involving text. NLP can streamline tasks, increase productivity, and reduce costs. In addition, these models can find patterns in high-volume, high-velocity datasets that would otherwise be very difficult to comprehend, allowing for data-driven decision making. When looking to harness the power of NLP, it is important to consider the scale of the problem, if the problem can be broken down into discrete subtasks, and requirements for accuracy. If applied to the right problem, NLP can deliver incredible value by adding efficiency, saving time and money, and unlocking new insights.
About the Author
Domenic Puzio is a Senior Machine Learning Engineer on the NLP Team at Kensho Technologies with over seven years of machine learning experience. He is the Tech Lead for Kensho NERD, a tool that recognizes important entities in unstructured text and links them to profiles in various databases. Domenic studied Mathematics and Computer Science at the University of Virginia, and he holds a Masters Degree in Computer Science with a specialization in Machine Learning from Georgia Tech. He has spent his career building and productizing machine learning models for cybersecurity, national security, and finance. Domenic is currently exploring the applications of large language models for various NLP problems.
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: https://twitter.com/InsideBigData1
Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/
Join us on Facebook: https://www.facebook.com/insideBIGDATANOW
Speak Your Mind