Natural Language Processing: Exploring the Highlights and Pitfalls

Robot hand and human hand reaching out to each other

What is natural language processing? 

Computers help us in so many ways, including speeding up processes, reducing human error in some tasks and managing dizzying amounts of data. 

In essence, computers use a different language than humans. How we would communicate with other people and how we communicate with machines are worlds apart. Natural Language Processing, also known as NLP, seeks to close that gap by providing a common form of communication that can be understood by us and computer systems. 

Natural language processing is a fascinating discipline where the wonderful worlds of linguistics, computer science and artificial intelligence (AI) collide. 

NLP seeks to make sense of human language in all its forms, allowing a more ‘natural’ way for people to interact with machines. NLP removes the need for users to know computer code in order to execute certain commands or tasks and can dramatically improve the efficiency of working with unstructured data.  

NLP seeks to not only comprehend the actual words being used but also attempts to decipher the sentiment and tone that is being conveyed – something humans process naturally when communicating with each other. This represents a tough challenge for NLP technology as languages and ways of communication are very nuanced. 

Applications of natural language processing 

Two halves of the brain one with computer network the other creative

The applications for natural language processing are far-reaching and continue to advance every year. NLP uses the ever-growing volumes of data accessible to continuously learn and improve. Below are some of the key applications of NLP that are benefitting businesses, organisations, and individuals. 

Data analysis 

Organisations are flooded with an ever-increasing deluge of data which can hold captivating and valuable insight but can also be a huge challenge to work with. NLP can help businesses to sort, mine and make sense of information in an efficient way. For example, NLP can help to sort your emails into folders based on specific rules and channel customer support queries into the correct workflow. 

NLP can also be a useful tool in speeding up the creation and quality of documents. This includes the ability to quickly summarise substantial portions of text, as well as improving the written word with spelling, grammar, tone, and other helpful suggestions. 

Sentiment analysis 

Sentiment analysis is another useful application that can help to identify and categorise text based on whether the attitude expressed is positive, neutral or negative. This can dramatically speed up the task of social media monitoring or the analysis of large quantitative market research studies, which would be inefficient or simply not feasible without technical support.  

However, sarcasm and slang can be confusing for people and NLP alike. The old-school example of “that’s sick” can have a wide array of meanings which without additional context could be easily misunderstood. 

Speech recognition and voice-activated technology 

Voice-activated devices continue to grow in popularity, with two-fifths of adults in England owning and using a voice-activated personal assistant or smart speaker device. The applications can range from simple commands, ‘lights on’, to more complex requirements such as dictating an email.  

NLP is a vital aspect of speech recognition and voice-activated technology allowing the interpretation of speech and voice commands. The introduction of NLP not only helps to speed up some processes but also allows greater flexibility in the way people choose to work and interact. NLP is also instrumental in helping to create more inclusive and accessible experiences by enabling features such as captions, subtitles, and transcription.  

Language translation 

Book showing hello translated into different languages

NLP is used to automatically translate written text or the spoken word from one language to another. It can also provide the output in your chosen variation of a language such as UK or US English. The quality of the translation varies by language. Common languages that are widely used tend to be translated more accurately as more data is available to improve the process over time.  

Regardless of the language, NLP translation often benefits from human intervention to improve phrasing and provide a more natural-sounding output to a native speaker. 

Examples of natural language processing 

If you’re unsure whether you’ve interacted with NLP in the past, these natural language processing examples will reveal some of the everyday uses that may surprise you.  

Google search 

That’s right. Every time you type into that search box Google is using NLP to help determine what you’re looking for and offer up the best results to help you. The same is true for the other search engines out there. 

In the past, Google was less sophisticated and search results were dependent on the exact words you used. Nowadays, you can ask Google a question in the same way you might ask a friend. Conversational search is more commonplace and yields impressive results. Using NLP, Google is more advanced at interpreting your query by being able to recognise not just the words themselves but the relationship between those words and phrases. 

Voice assistants 

Voice activated assistants

When you use Alexa to blast out your favourite tune, Google Voice to turn on your lights, or Siri to make a call, your handy voice assistants are using voice recognition technology combined with NLP to perform your every request.  

Predictive text and autocorrect 

When you’re on your phone, computer or tablet, predictive text and autocorrect can be a time-saving tools to help you craft messages or other written content. The technology won’t always get it right, but it can certainly help when you miss hit keys or have brain fog on how to spell a particular word. 

In business and education, autocorrect functionality can help users to produce higher-quality outputs. Spelling and grammar mistakes can be reduced through services such as Grammarly or other tools readily available through word editors. 

You may have noticed that over time the NLP tech starts to recognise common words or phrases you use. Using this information, the output can be more personalised to you, with predicted sentences becoming a closer reflection of your natural phrasing. 

Automated phone calls 

Some automated phone systems still rely on you tapping the relevant key number to proceed, but many have started to introduce the ability to state your query instead. The technology will interpret what you say and direct you to the best place to get help. 

You may also hear the phrase ‘Calls are recorded for quality monitoring purposes’. By recording the call, analysis can be carried out to identify common customer problems, training requirements and the sentiment of people ringing in. NLP can be used in the analysis of call recordings, but many businesses choose to rely on more accurate voicemail transcription services instead.  


ChatGPT has been a hot topic across the internet, and you’ve guessed it, natural language processing is used to generate conversations. The wide-reaching uses for ChatGTP continue to be debated, but one thing is clear, people have been impressed by the human-like conversations it is able to produce in a matter of seconds. 

Chatbots are also commonly used as a customer service tool. Customers can be funnelled to the best resources to help them based on their query, such as help articles, or directed to the relevant department to solve their issue. The introduction of chatbot workflows can help to reduce the level of expensive human resources required to support customers. Although, it should be noted that some experiences are not always seamless from the customers’ perspective. 


Auto-captions across social media and video platforms are now prevalent, helping content creators to provide more accessible and engaging content. Auto-captions are generated using automatic speech recognition (ASR) technology which harnesses NLP to transform the spoken word into text. 

With some NLP examples discussed the topics and likely scenarios are pre-defined. For example, there are a limited number of reasons you would have for calling your utility provider. However, a YouTube video or social media post could be about any topic. This makes the scope for error much greater. 

Auto-captions and auto-transcription often have low levels of accuracy which require heavy editing to make them usable. Services that rely solely on ASR technology fall below the levels of accuracy required for businesses and accessibility. Alternatively, human and hybrid transcription services can provide guarantees of at least 99% accuracy for captions and subtitles 

Pitfalls of Natural Language Processing 

Friendly looking robot

The examples demonstrate some of the great wins we are experiencing due to the application of NLP. However, it is wise to remember that NLP is not a perfect solution and does have some significant pitfalls. Understanding the challenges can help you to use NLP to your advantage while understanding where you might need additional intervention. 

Language is complex and evolves over time. And context is paramount. NLP is trying to make sense of written text or the spoken word and interpret the context. There are some hurdles that haven’t been cracked yet. 

Speech to text 

Significant issues exist for speech-to-text applications as speech patterns vary and the quality of the audio influences results. Poor audio with significant background noise, multiple speakers talking over each other, or heavy accents all make the automatic conversion to text extremely challenging. Individuals might speak very quickly, trip over certain words, slur, or mispronounce words, which all adds to the challenge of accurate interpretation.  

When transcribing audio or video content, using a professional transcription service, like Take Note, which elicits human intervention yields much higher-quality results.  


Informal language is constantly in flux with new terms being introduced and differences seen at a local level. The flexibility in the meaning of slang words makes it extremely difficult for NLP systems to interpret. 


Humans can struggle to identify sarcasm, so how are machines supposed to cope? 

Sarcasm is used frequently in conversation and is prevalent across comments on social media too. Tweets can be meant sarcastically way but interpreted by the reader in a vastly different manner. Country, background, upbringing, and many other factors can influence can intended sarcasm is understood. As you might expect, sarcasm can play havoc with sentiment analysis. 

Acronyms and specialist languages 

In business, acronyms can fly about but only make sense to those that use them every day. An organisation that uses very specialist, technical or bespoke language would benefit from using a tailored model that can be trained to help the NLP operate effectively in that setting.  

The great news is that the more data is available the better the systems that rely on natural language processing become. NLP has many benefits and use cases, but doesn’t always hit the mark. Relying solely on ASR for transcription and captions is one example where human intervention is still required to obtain the accuracy levels required. Reach out to Take Note to get a quote today for transcription that is at least 99% accurate, secure and with turnaround times guaranteed. 

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Kat Hounsell