While NLP has quite a long history of research beginning back in 1950, its numerous uses have emerged only recently. With the introduction of Google as the leading search engine, our world being more and more digitalised, and us being increasingly busy, NLP has crept into our lives almost unnoticed by people. Still, this is what’s behind the multiple conveniences in our day-to-day existence.

What is Natural Language Processing?

Natural Language Processing is all about mimicking and interpreting the complexity of our natural, spoken, conversational language. It’s a field of computational linguistics, which is a relatively new science.

What is NLP? It is a subfield of computer science and artificial intelligence (AI) that uses machine learning to enable computers to understand and communicate with human language. While this seems like a simple task, it’s something that researchers have been scratching their heads about for almost 70 years.

Still, with tremendous amounts of data available at our fingertips, NLP has become far easier. The more data you analyse, the better the algorithms will be. The growth of NLP is accelerated even more due to the constant advances in processing power.

Similarly to AI specialists, NLP researchers and scientists are trying to incorporate this technology into as many aspects as possible. Modern NLP technologies, particularly deep learning models, have revolutionised the field by using neural network models to train NLP systems from diverse and large datasets, enabling transfer learning to achieve new tasks with less data and compute effort.

Even though NLP has grown significantly since its humble beginnings, industry experts say that its implementation still remains one of the biggest big data challenges.

Before putting NLP into use, you’ll need data. By using information retrieval software, you can scrape large portions of the internet.

Want to know more about NLP? Take a look here:

• How is Natural Language Processing (NLP) used in business?
• How do NLP and IDP address business challenges?
• NLP techniques: key methods that will improve your analysis

NLP consists of two fundamental tasks: syntax analysis and semantic analysis.

Syntax analysis

Syntax analysis is used to establish the meaning by looking at the grammar behind a sentence. Also called parsing, this is the process of structuring the text using grammatical conventions of language.

Essentially, it consists of the analysis of sentences by splitting them into groups of words and phrases that create a correct sentence. Syntax analysis is one of the fundamental NLP tasks in processing human text and voice data.

This doesn’t account for the fact that the sentences can be meaningless, which is the point where semantic analysis comes with a helping hand.

Semantic analysis

Our understanding of language is based on the years of listening to it and knowing the context and meaning. Computers operate using various programming languages, in which the rules for semantics are pretty much set in stone. Now, human language is different, as it is dynamic.

With the invention of machine learning algorithms and advancements in natural language understanding, computers became able to comprehend and generate human language. At least to a certain degree.

While syntax analysis is far easier with the available lexicons and established rules, semantic analysis is a much tougher task for the machines. Meaning within human languages is fluid, and it depends on the context in many situations.

For example, Google is getting better and better at understanding the search intent behind a query entered into the engine. Still, it’s not perfect. I bet that you’ve encountered a situation where you entered a specific query and still didn’t get what you were looking for.

NLP helps with that to a great degree, though neural networks can only get so accurate.

The benefits of NLP

Natural Language Processing (NLP) offers numerous benefits across various fields and industries:

1. Improved communication: NLP enables more natural human-computer interaction through voice assistants, chatbots, and language translation services, breaking down language barriers.
2. Efficient information extraction: It allows for quick analysis of large volumes of unstructured text data, extracting key insights and trends that would be time-consuming for humans to process manually.
3. Streamlined business operations: By automating text-heavy tasks like document classification, email filtering, and report generation, NLP increases operational efficiency and reduces human error.
4. Advanced search capabilities: NLP improves search engine performance, enabling more accurate and context-aware results, benefiting both users and content creators.
5. Market Intelligence: NLP tools help businesses gain valuable insights from social media, reviews, and news articles, informing strategic decisions and product development.
6. Accessibility: Text-to-speech and speech-to-text technologies, powered by NLP, make digital content more accessible to people with visual or auditory impairments.
7. Research and Development: NLP accelerates scientific research by helping researchers quickly sift through and analyse large volumes of academic papers and data.

These benefits demonstrate NLP’s wide-ranging impact, improving efficiency, accuracy, and accessibility across numerous domains.

How does NLP work?

There are numerous techniques associated with Natural Language Processing, including machine learning methods. Each of them is different, though they can provide you with invaluable insights concerning your data when used together.

These techniques also reduce the time it takes to process data by removing and simplifying particular elements of sentences.

Find out how NLP is used in practice in different areas:

• NLP in the insurance industry: top 5 use cases and benefits
• How is NLP transforming finance, FinTech, and banking?

Sentiment Analysis or Opinion Mining

Sentiment analysis is the investigation of statements in terms of their — as the name suggests —sentiment. In essence, it consists of determining whether a portion of text has a positive, negative, or neutral attitude towards a certain topic.

Now, the more sophisticated algorithms are able to discern the emotions behind the statement. Sadness, anger, happiness, anxiety, negativity — strong feelings can be recognised. It’s widely used in marketing to discover the attitude towards products, events, people, brands, etc.

Data science services are keen on the development of sentiment analysis, as it’s one of the most popular NLP use cases.

Parsing

Parsing is all about splitting a sentence into its components to find out its meaning. By looking into relationships between certain words, algorithms are able to establish exactly what their structure is.

Stemming and Lemmatisation

Stemming is a method of reducing the usage of processing power, thus shortening the analysis time. Stemming converts words into their roots, e.g. “buying” will be converted to “buy.” Consider the sentences “I’ll be buying some shoes,” and “I will buy some shoes.”

They have the same meaning, so the algorithm reduces the first infinitive one to its stem, decreasing the amount of data needed to analyse.

Lemmatisation differs a bit from stemming in that it reduces words into their most basic forms.

Imagine that you’re looking into terabytes of information to gather insights. Such situations will occur fairly frequently, and the amount of time you save is significant.

Named Entity Recognition (NER)

Named Entity Recognition (NER) is the process of matching named entities with pre-defined categories. It consists of first detecting the named entity and then simply assigning a category to it.

Some of the most widely-used classifications include people, companies, time, and locations. NER is one of the many NLP tasks that involve processing human text and voice data.

NER is helpful when you need an overview of immense amounts of writing.

Relationship Extraction

The Relationship Extraction process takes named entities from a text and then recognises the relationship between them.

For example, you could ask Google, “who is the chairman of Intel,” and the algorithm, using RE, would associate the relationship between “chairman” and “Intel,” providing you with the correct answer.

RE could also be used when you analyse large portions of customer service queries. It allows for the detection of particular relationships and categorises them in terms of priority. This, in turn, facilitates your support tasks and improves customer experience.

Topic Modeling and Classification

Topic Modeling is most commonly used to cluster keywords into groups based on their patterns and similar expressions. It’s a technique that is entirely automatic and unsupervised, meaning that it doesn’t require pre-defined conditions and human ability.

On the other hand, Topic Classification needs you to provide the algorithm with a set of topics within the text prior to the analysis. While modelling is more convenient, it doesn’t give you as accurate results as classification does.

Stop Word Removal

One of the essential elements of NLP, Stop Words Removal gets rid of words that provide you with little semantic value. Usually, it removes prepositions and conjunctions, but also words like “is,” “my,” “I,” etc.

Tokenisation

Tokenisation is the process of breaking down text into smaller units called tokens. These tokens are typically words, numbers, or punctuation marks, but can also be subwords or characters depending on the specific application.

Tokenisation can use various methods:

• White space tokenisation – splits text at spaces (simplest method, but can be inaccurate for languages without clear word boundaries).
• Rule-based tokenisation – uses predefined rules to identify word boundaries.
• Regular expression tokenisation – employs regex patterns to split text.
• Machine learning-based tokenisation – uses trained models to identify tokens, especially useful for complex languages.

Tokenisation is crucial in NLP technology as it’s often the first step in text processing, forming the foundation for further analysis like part-of-speech tagging, NER, or sentiment analysis.

What are some common applications of NLP?

NLP has a lot of uses within the branch of data science, which then translates to other fields, especially in terms of business value.

Speech recognition

NLP is what lies behind speech recognition software. By analysing speech patterns, meaning, relationships, and classification of words, the algorithm is able to assemble the statement into a complete sentence.

Using deep learning, you also get to “teach” the machine to recognise your accent or speech impairments to be more accurate.

Additionally, the technology called Interactive Voice Response allows disabled people to communicate with machines much more easily.

Market analysis

NLP allows companies to determine current trends by analysing large amounts of available data. Using Topic Classification, the machine can find out what categories are the most common.

Social media analysis, for example, can provide you with insights concerning your industry, product, or brand straight from the consumers’ point of view, which improves your business intelligence.

You get to see what the sentiment is, which topics are the most usually talked about, what the opinion about your competitors is, the latest trends, and so on. And what better source of information than your audience?

Predictive text

NLP finds its use in day-to-day messaging by providing us with predictions about what we want to write. It allows applications to learn the way we write and improves functionality by giving us accurate recommendations for the next words.

Language translation

Online translators wouldn’t be possible without NLP. Remember a few years ago when software could only translate short sentences and individual words accurately? Well, that’s history.

For example, Google Translate, which uses machine translation, can convert entire pages fairly correctly to and from virtually any language.

Disease prediction

NLP is widely used in healthcare as a tool for making predictions of possible diseases. NLP algorithms can provide doctors with information concerning progressing illnesses such as depression or schizophrenia by interpreting speech patterns.

Still, psychiatry is not the only field of medicine that NLP finds use in. Natural language generation can also be used to summarise medical information into text.

Medical records are a tremendous source of information, and practitioners use NLP to detect diseases, improve the understanding of patients, facilitate care delivery, and cut costs.

Search Engine Optimisation (SEO)

With NLP and BERT interconnected, the entire field of SEO has undergone considerable changes following the 2019 update. Context, search intent, and sentiment are currently far more important than they’ve been in the past. BERT has impacted about 10% of all queries, which is a tremendous number.

Google has incorporated BERT mainly because as many as 15% of queries entered daily have never been used before. As such, the algorithm doesn’t have much data regarding these queries, and NLP helps tremendously with establishing the intent.

Challenges of Natural Language Processing (NLP)

One of the primary difficulties is dealing with the inherent ambiguity in language, where words and phrases can have multiple meanings depending on context. This ambiguity extends to sentence structure, idioms, and cultural references, making it challenging for machines to accurately interpret human communication.

Another significant hurdle is the vast diversity of languages and dialects worldwide. Each language has its unique grammatical rules, syntax, and semantic nuances, requiring specialised models and approaches. Moreover, many languages lack extensive digital resources, making it difficult to train robust NLP models for them.

Context understanding presents another major challenge. NLP systems often struggle to grasp the broader context of a conversation or document, which is crucial for accurate interpretation. This includes understanding sarcasm, humor, and implicit information that humans easily infer but machines find difficult to detect.

Handling informal language, including slang, abbreviations, and evolving internet language, poses ongoing difficulties. These linguistic elements change rapidly, making it hard for NLP models to stay current.

Bias in language models is a growing concern. NLP systems can inadvertently perpetuate or amplify biases present in their training data, leading to unfair or discriminatory outcomes. Addressing this requires careful consideration of data sources and model design.

Multimodal NLP, which involves integrating language processing with other forms of data like images or audio, presents its own set of challenges in aligning and interpreting diverse data types.

Finally, the computational resources required for advanced NLP tasks, especially for large language models, can be substantial. Balancing model performance with efficiency and accessibility remains an ongoing challenge in the field.

Do you want to use the potential of NLP in your business?

Natural Language Processing Market is set to surge from a valuation of $29.1 billion in 2023 to $92.7 billion by 2028, demonstrating a Compound Annual Growth Rate (CAGR) of 26.1% during this period (2023 to 2028).

With the available information constantly growing in size and increasingly sophisticated, accurate algorithms, NLP is surely going to grow in popularity. It’s altering the way of interaction between humans and machines. The previously mentioned uses of NLP are proof of the fact that it’s a technology that improves our quality of life by a significant margin.

As much as 80% of the information that surrounds us is unstructured. For this reason, NLP is one of the largest fields of data science.

Organising this data is a considerable challenge that’s being tackled daily by countless researchers. Continuous advancements are being made in the area of NLP, and we can expect it to affect more and more aspects of our lives.

And if you are looking for a consultation or partner to implement solutions in the areas of AI/ML, Data services or related areas – give us a call!