Canva The use of NLP in everyday applications has grown exponentially over the past few years. You can find it in chatbots, voice search, and voice assistants like Alexa, Siri, and Google Assistant. NLP is used in various industries, including healthcare, business analytics, business intelligence, customer service, education, entertainment, and games development. This article will cover some of the biggest challenges faced by NLP today.

1. Taking Context Into Account

It is crucial to understand the context of a word. For example, in the sentence “We saw him talking to his friends,” the word “talked” can be replaced by “chatted,” depending on whether he was talking to his friends casually or as part of a serious conversation. Context matters when it comes to interpreting what people say and writing them meaningful messages back. If you’re looking for proof of this, look no further than Google’s virtual assistant, Google Assistant—which uses natural language processing (NLP) algorithms to parse your text input and offer suggestions on how best to respond. The problem with NLP today is that it doesn’t consider the context when making suggestions. So rather than suggesting a response based on what you’ve said so far (and where), we have an algorithm that looks at individual words independently from one another—an approach known as shallow parsing. This means our machines cannot perform sophisticated analysis using information from multiple sources at once—something humans do naturally without even thinking about it.

2. Solving Ambiguous Language

Ambiguous language can be a massive challenge for any NLP system. This is especially true regarding natural language processing since humans often use vague language to express themselves differently. For example, “I went to the store” could mean that you went to the store on foot or by car, and it could also mean that you went shopping at a physical store or an online retailer. For an NLP system to be effective, it needs to recognize and accurately interpret all of these different forms of ambiguous language without getting confused or misinterpreting them in other ways.The solution is to use multiple forms of context. If an NLP system has two different sources of information about the same thing, then it can use those sources to determine whether or not it is correct in its interpretation.

3. Handling Code-Switching and Mixed Language Input

A big challenge for NLP systems is handling code-switching and mixed language input. Code-switching, when a speaker alternates between two languages in the same conversation, can be challenging to analyze because you might have to look at more than one language at once. There are many different ways that speech recognition can handle these situations; most involve using multiple or conditional models that train on specific parts of speech or character types (for example, Hausa/English). This allows your system to identify what part of speech should go with what other part of speech based on context clues from previous words (like “yesterday” or “bought”) and their relation to the last sentences. When it comes down to it, speech recognition is just another form of machine learning—and that’s why it’s so powerful.

4. Translating Languages and Local Dialects

The most difficult problem facing NLP is the process of translating languages. The challenge lies not only in the fact that there are thousands of known languages (estimates vary widely, but at least 5,000) but also in that some have far fewer speakers than others. For example, while English is spoken by an estimated 375 million people worldwide and Russian by around 190 million people (roughly twice as many), the latter has almost twice as many letters in its alphabet as the former. This makes it much harder to translate from Russian into English than vice versa: a word like “kommuna”—which translates roughly as “community center”—cannot be translated literally into English without losing meaning. The same goes for words like “gulag” (a network of Soviet-era prisons) or even simple grammatical concepts such as tense or gender agreement; they don’t exist in other languages.

5. Generalizing to New Domains and Data Types

As the field of NLP continues to grow and develop, it’s essential to remember the importance of generalizing to new domains and data types. The difficulty in generalizing your model comes from the fact that you are trying to learn how your model behaves on data that has yet to be seen before. To successfully generalize your model, you will need a solid understanding of what constitutes a suitable loss function and an intuitive understanding of why these parameters should be set at specific values before training them with your dataset. With this knowledge, you can begin fine-tuning these parameters to work well once trained on a new dataset or task type (aka domain).

6. Creating Appropriate Human-Machine Interfaces

To design a human-machine interface, you must first understand the cognitive abilities of humans and machines. This can be achieved through various methods, including observation of users performing tasks and analyzing how they solve problems. Next, review existing literature to determine how others have tackled similar issues in the past—using data science tools to analyze user behavior and predict future interactions with your product or service. In addition to understanding the cognitive abilities of humans and machines, you must also understand the limitations. For example, a human can only process so much information simultaneously; if your interface is too complex or includes too many options, users will get confused and frustrated. The same goes for machines: if you ask them to do something beyond their capabilities (like identifying faces in an image), they won’t be able to do it. The key to designing an interface that works for humans and machines is to identify your users’ cognitive abilities, understand the limitations of both humans and machines, and then plan your product or service accordingly.

7. Speech Synthesis That Sounds Natural

The creation of a natural-sounding voice is an incredibly challenging problem. Speech synthesis systems are constantly improving, but they still need to be improved, and there’s still much to be done before they can produce the same quality of speech as a human voice. To create a natural sound speech, you must pronounce words correctly and alter your intonation depending on what you’re saying. If you’re talking about someone who has passed away, for example, then your intonation will change as compared with if you were talking about something mundane like how much food is left in your fridge. This is a complex problem to solve, but it’s one that we’re making progress on. It has become much easier to produce near-perfect speech through the use of speech synthesis systems, and they are now able to produce near-perfect speech much more frequently than they were able to before. As a result, we’re starting to see speech synthesis systems used in real-world applications. You can now talk to your phone or computer and have it respond with near-human quality of speech that may even fool people into thinking you’re talking with another human being.

8. Dealing With Multi-Modal Input (Audio/Video)

The next big challenge is dealing with multi-modal input. We’re now seeing data coming in from various sources, such as audio, video, and text. But how do you combine these different data types into a single model? This is an important question because the challenge is more than adding more feature types. It’s also about creating an effective architecture that simultaneously handles all this information. Ideally, this problem could be solved by creating one model that takes both audio signals and images simultaneously. To do this, you can use a convolutional neural network. This type of neural network is particularly good at processing images because it has learned to recognize features in pictures and make connections between them. In addition, a convolutional neural network can be used to analyze both audio signals and images simultaneously, which means that it’s much more effective than other models when dealing with multi-modal input.

9. Creating Meaningful Visualizations of Text Data

The first challenge is the ability to visualize text data. If a textual dataset is pictured correctly, it’s easier for a human to understand what trends might exist within that dataset. This is especially true if you deal with millions or billions of rows of text data (like social media posts). To create meaningful visualizations of text data, you’ll need the following:

A way to analyze your dataset and determine what types of graphs will be most effective at highlighting relationships between different words/phrases in your document. A toolkit that allows you to easily create these custom graphs by providing an intuitive interface for interacting with and modifying graph elements. This toolkit should also allow you to export these custom graphs as PNGs or SVG files so they can be embedded into other web pages online or shared on social media platforms like Facebook or Twitter. A way you can highlight certain words in your document and create a graph showing how often they appear together in the same sentence, paragraph, or page. A way you can highlight specific keywords in your document and create a graph showing how often they appear together relative to other words/phrases on any given page. This could be useful for highlighting which words are most associated with each other within specific contexts (i.e., in what ways do these two words co-occur more than others?).

10. Handling Actual World Text Data Effectively

You’ll also want to understand that real-world text data is essential for NLP, but it has challenges. The most common challenge for NLP when working with real-world text data is noise. When you get your hands on a vast corpus of text data and start preparing it for training, you’ll need to know how to handle all the noise and irrelevant information that comes along with it. Noise can make certain words less valuable or even unusable in your model’s training process because there’s so much other stuff mixed in there as well–it can be tough to tell what matters and what doesn’t. It’s essential not only for NLP models trained on real-world data but also for those trained on datasets compiled from other sources like news articles or tweets (which themselves may contain plenty of noise). You’ll need ways of filtering out the unnecessary info without losing any key features used by the model itself.


NLP has come a long way from its early days. In the early days of machine learning, the focus was on building models and developing statistical methods. Today, many people are looking at natural language processing from a more conceptual perspective where they can think about how language works and everything else (i.e., not just text). I hope this article has been helpful for you and that you can now think about NLP more conceptually. There are many more topics related to natural language processing that I will be covering soon. This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional. © 2022 Hassan

10 Biggest Challenges Faced by NLP Engineers - 1510 Biggest Challenges Faced by NLP Engineers - 9710 Biggest Challenges Faced by NLP Engineers - 9010 Biggest Challenges Faced by NLP Engineers - 94