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10 Major Challenges of Using Natural Language Processing


October 8, 2021

Natural Language Processing

Alexa and Siri, email and text predictive text, and customer support chatbots are all examples of AI technology in our daily lives. They all interpret, “understand,” and react to human language, both spoken and written, using machine learning algorithms and natural language processing (NLP).

While natural language processing (NLP) and its sister discipline, natural language understanding (NLU), are continuously improving their capacity to compute letters and sentences, human language is immensely complex, fluid, and inconsistent, posing major obstacles that NLP has yet to fully overcome.


Major Challenges of Using NLP

The majority of the difficulties come from data complexity, as well as features like sparsity, variety, dimensionality, and the dynamic properties of the datasets. NLP is still a young technology, therefore there is a lot of room for engineers and businesses to tackle the numerous unsolved problems that come with deploying NLP systems.

Let’s take a look at some of those challenges in more detail below.


Development Time

In a similar vein, you should consider the time it takes to create an NLP system. An AI needs to evaluate millions of data points to be adequately trained; processing all of that data might take a lifetime if you’re using an underpowered PC. With a shared deep network and many GPUs working together, that training period may be reduced to just a few hours. However, unless you’re using pre-existing NLP technologies, you’ll need to figure in time to build the product from the ground up.


Phrasing Ambiguities

Still another human being finds it difficult to decipher what someone intends when someone says something vague. In a careful study of their statements, there will not be a clear, succinct meaning to be found. To address this problem, an NLP system has to be able to look for context that will assist it to comprehend the phrase. It may sometimes be necessary to seek clarification from the user.



Misspellings are an easy challenge for humans to solve; we can quickly link a misspelt word with its correctly spelt equivalent and understand the remainder of the phrase. Misspellings, on the other hand, can be more difficult for a machine to detect. You’ll need to employ a natural language processing (NLP) technology that can identify and progress beyond typical misspellings of terms.


Language Differences

The majority of people in the US speak English, but if you want to reach a global and/or diverse audience, you’ll have to support various languages. Not only do various languages have substantially diverse collections of vocabulary, but they also have different forms of phrasing, inflections, and cultural norms. You can overcome this problem by using “universal” models that can move at least part of what you’ve learned to other languages. You will, however, have to spend time updating your NLP system for each additional language.


Training Data

NLP is all about studying language in order to better comprehend it. To become proficient in a language, a person must be immersed in it continually for years; even the greatest AI must spend a substantial amount of time reading, listening to, and using the language. The training data given to an NLP system determines its capabilities. If you feed the system inaccurate or skewed data, it will learn the incorrect things or learn inefficiently.


Innate Biases

In certain situations, the biases of its programmers, and also biases in the data sets used to develop them, might be carried by NLP systems. An NLP might exploit and/or perpetuate certain social prejudices, or give a superior experience to some types of users over others, based on the app. It’s difficult to create a solution that operates in all situations and with all the people.


Words with Multiple Meanings

There is no such thing as flawless language, and most languages include words that can have several meanings depending on the situation. A user who inquires, “How are you?” has a very different aim than a user who inquires, “How do I add the new debit card?” Good NLP tools should be able to discriminate between these utterances with the help of settings.


Phrases with Multiple Intentions

Because certain words and queries have many meanings, your NLP system won’t be able to oversimplify the issue by understanding simply one of them. A user may say to your chatbot, “I have to cancel my prior order and change my card on file,” for instance. Your AI must be able to discern between these intentions.


Uncertainty and False Positives

When an NLP detects a term that should be intelligible and/or addressable but can’t be adequately replied to, it’s called a false positive. The idea is to create an NLP system that can identify its own limits and clear up uncertainty using questions or hints.


Keeping a Conversation Moving

Many current NLP applications are based on human-machine communication. As a result, your NLP AI must be able to keep the dialogue going by asking more questions to gather more data and constantly pointing to a solution.



While natural language processing has its drawbacks, it still provides a lot of advantages for every company. Many of these obstacles will be torn down in the next years as new approaches and technology emerge on a daily basis. Machine learning techniques based on natural language processing may be used to evaluate large quantities of text in real-time for previously unobtainable insights.

If you’re managing a project utilizing NLP, one of the simplest ways to tackle these problems is to use a collection of NLP tools that already exist and can help you solve some of these hurdles quickly. Utilize the efforts and creativity of others to provide a better product for your consumers.

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