The NLP Manual: The Basics Explained and Real Life Problems it Solves

If you’re reading this, chances are you have just discovered NLP, or natural language processing to give it its full name. 

NLP is a field of AI that is used widely across many technologies, from spam detection to voice assistants such as Alexa or Siri. 

In this manual, we’ll cover everything you need to know about the foundations of NLP.

What is NLP?

The field of NLP combines linguistics with machine learning to enable computers to understand and generate human language. NLP is used to power technology such as chatbots, data analysis, translations and sentiment analysis. 

What is NLP used for?

Common applications for NLP include:

Text classification

Text classification involves the sorting of text into specific categories, such as spam versus non-spam emails.

• Proofreading
• Summarization

Sentiment analysis

This usage of NLP determines the sentiment and opinions expressed with text, categorising it into either positive, negative or neutral.

• Social media monitoring
• Customer feedback analysis

Translation

NLP can convert text from one language into another, as well as detecting the language expressed within text.

Chatbots and virtual assistants

One of the most popular usages of NLP is to power online chatbots and virtual assistants for websites and apps.

• Question answering
• Search engines

How NLP has evolved over the years

Overcoming contextual barriers

Whilst many applications that are powered by NLP are simple and user friendly, the technology itself is rather complicated, necessitated by the complexity of the textual issues it faces.

For example, words or sentences can have multiple meanings. Take the phrase “I saw the man with the telescope”, this can mean either:

• I saw a man who had a telescope
• I used a telescope to see the man

This ambiguity can cause confusion even to a human reader or listener, let alone a computer.

Understanding the context of a statement is the key to understanding the true intended meaning.

Early NLP models struggled with understanding context, but the industry evolved at an impressive rate. Tools such as Google’s BERT (Bidirectional Encoder representations from transformers) were developed to better understand context. 

BERT processes the text ‘bidirectionally’, meaning that it considers the context both to the left and right of the content. This extra contextual analysis makes BERT highly effective for tasks such as answering questions and text classification.

Large datasets

Even today, training an NLP model requires large datasets and significant computational power. This is because the training process involves a vast amount of text. 

Whilst large data analysis remains a feature of NLP development, new solutions such as cloud-based training and distributed processing frameworks such as Apache Spark, Dask, and Ray are helping to overcome these data challenges. 

Even with large datasets, many early NLP models struggled with uncommon or rare words that had not been included in their training dataset, especially slang. To overcome this issue, many NLP model developers will use a combination of:

• Subword tokenization, a strategy that breaks words into smaller subwords that the model may be able to understand, even if the full word remains unfamiliar
• Transfer training involves training NLP modes on informal text, such as that used on social media where slang is more commonly used. This exposure to slang during the training phase will help the NLP models to learn the meanings and patterns involved in slang.
• Continual training will help the model to stay up to date with contemporary and current slang
• Community feedback, often using feedback from users, can help to correct misinterpretations and incorrect responses from NLP models

You can read about the evolution of text analysis to observe how far it has come due to the latest developments within natural language processing.

Industries that utilize NLP

Healthcare

The healthcare industry has adopted NLP across many usages, focusing upon streamlining the patient experience. 

From analysis of electronic health records to patient communication chatbots, the healthcare industry has adopted NLP primarily to extract key insights into patient health from large swathes of data.

Customer service

NLP is being deployed to develop a more intelligent, responsive and user-friendly chatbot. Early iterations of chatbots struggled to provide personalized, useful responses to enquiries. 

Contemporary AI-powered chatbots have an improved understanding of user intent as well as enhanced language comprehension. These two features combine to provide the chatbots with a deeper understanding of the queries they are being tasked with. 

Banking

The banking and financial services industry have adopted NLP to handle account inquiries and to perform automated fraud detection for transactions. By doing so, they are helping to keep their customers safe when banking online. 

One of the key challenges that financial institutions face when developing NLP banking solutions is the requirement for the utmost level of cybersecurity. The solutions, such as chatbots or virtual assistants, must be able to withstand malicious attacks to extract information. 

To achieve this, the NLP solutions must be trained to have defined rules about what information can be accessed and shared. For example, sensitive information such as a person’s account details, balance or transaction history must never be shared. 

Education

NLP solutions such as chatbots can provide tailored learning solutions for students.

To train an educational chatbot, the sources of information must be tightly controlled to ensure that only true statements and facts are used. 

The more advanced education NLP solutions are able to understand the context of the inputted answers, enabling the chatbot to identify possible reasons why the student may be incorrect. This allows the chatbot to provide personalized feedback to the student. 

Similarly, NLP-powered chatbots can be trained to adapt their tone and complexity level to suit the student.

Emerging trends in NLP

The future of NLP is looking bright, with so many firms and industries dedicating resources to development.

Multimodal NLP represents the emerging breakthrough. It refers to the concurrent processing of multiple forms of data, such as text, images and audio, which will create systems that can understand and generate language using context from multiple modalities at once. This will open up new applications and improve user experience for tasks such as speech-to-text and translation.

These multimodal NLP solutions are extremely complex, and there are a number of data-related challenges regarding the integration and alignment of the different modalities. 

Building custom NLP solutions

At NetGeist, we develop NLP solutions for businesses and government entities around the world. Whether you need help with custom data training specifically for your industry, through to front end design or anything in between, we are here to help. Chat with our Assistant Robert to get started with your custom NLP solution.