What is Natural Language Processing and how is it used in business?
Natural Language Processing is an increasingly hot topic in the era of growing popularity of artificial intelligence – although computational linguistics is several dozen years old. What is NLP and how is it used in everyday life and business applications? How exactly does natural language processing technology work? You’ll find out in a moment by reading on.
What is Natural Language Processing (NLP)?
What does NLP mean? Natural Language Processing, in short, is the ability of a computer program to understand human language – and it can be written or spoken. This is a concept from the field of artificial intelligence.
Natural language processing allows computers not only to understand it (Natural Language Understanding, NLU), but also to create it (Natural Language Generation, NLG).
Currently, many technologies and solutions available in business and everyday life are based on this technology. Examples include text and voice virtual assistants such as chatbots and voicebots that deal with customers talking to them (as well as assistants that “live” in our phones, such as Siri or Alexa), ChatGPT, spam filtering, an internet search engine that suggests related terms, automatic text translation or grammar checking.
NLP is also used in e-mail, where it provides a suggested response to the message based on what is written in the message. Many companies have databases of unstructured text data that can only be effectively processed through Natural Language Processing.
Natural Language Processing (NLP) is used in many industries and business branches, which we will talk about in detail later.
But first, let’s say a few words about how the technology behind NLP works.
How does Natural Language Processing work?
NLP makes computers understand natural language – just like humans. They take input using text readers and microphones (just like humans use their senses). The input is then converted into code that the computer understands.
Development of Natural Language Processing methods
The history of Natural Language Processing began in the 1950s, with the Turing test, which was designed to check whether a computer could be truly intelligent – the condition for a successful test was the understanding and generation of natural language.
Until the 1990s, Natural Language Processing was based on a set of linguistic rules that, when strictly followed, allowed computers to understand the structure and meaning of human language.
In the 1990s, this approach began to be replaced by machine learning – algorithms that use statistical methods. They learn to perform tasks based on the training data they receive and adapt their methods with the inflow of new data – generating predictions based on generalization of the given examples.
Such a system uses the so-called neural networks and deep learning, thanks to which its operation in some sense resembles the operation of the human brain, and the algorithms – by discovering subsequent regularities and patterns in the data – are able to learn and improve their operation.
However, it is important that there is enough linguistic data – this possibility is provided, for example, by a search engine into which people all over the world constantly enter queries. It is also worth giving an example related to sentiment analysis – the algorithm receives examples of sentences with a positive, negative or neutral character and classifies other examples on this basis.
Since 2000, natural language processing has been gaining popularity. Along with discoveries in the field of computer science, it has also gained a number of practical applications. Today, the approach to NLP includes a combination of classical linguistics and statistical methods, with the latter increasingly predominating.
Neural networks are currently trained with huge amounts of available data, which could raise concerns about excessive load, but there is a way – the algorithm initially learns on a large set of data, and then is fine-tuned based on a smaller set, closely related to the given task.
Natural Language Processing techniques
To process natural language, it must be structured into elements that computers can understand. First, you need to pre-prepare and “clean” the text data (or speech converted to text) so that the machine can analyze it – simplify it to a version that will be understandable by the computer. The language is then further processed using further techniques and algorithm development.
To begin with, tokenization is applied – in this case, the text is broken into smaller fragments – so-called tokens, i.e. words, their parts (prefixes, suffixes) or word sequences. These are often repeating sequences that then serve as units for further processing.
Also stop words removal is applied: this is the removal of tokens that will be ignored during processing. These are short and frequently occurring words, for example “that” or “and”, which do not carry much meaning. This reduces processing time, although this technique is not always used because its use tends to miss subtle differences in meaning.
It is also worth mentioning “bag of words” models here, which – although they constitute unordered sets of tokens – can be useful as an efficient, simple tool for the initial analysis of large amounts of information.
Natural Language Processing uses advanced linguistic techniques that allow you to process human language. The two main groups of techniques are syntax and semantic analysis.
Syntax involves arranging words into a sentence that makes sense grammatically. The methods that can be used, are, among others:
- Parsing, i.e. grammatical analysis of a sentence
- Word segmentation, i.e. the analysis of how the text is divided into words
- Sentence splitting, i.e. breaking the text into sentences, divided, for example, by dots
- Morphological segmentation, i.e. the division of words into smaller particles – morphemes
- Stemming and lemmatization – reducing words to their basic form and grouping them, which allows you to organize the text even better
- Part-of-speech tagging, i.e. marking each word according to the part of speech it represents (for example: noun, verb, adjective)
Semantic analysis techniques, on the other hand, focus on the contextual meaning of words. They are so complex that they are still in the development phase. This is for example:
- Word sense disambiguation – determining what the meaning of a word is due to the context in which it is used (in relation to homonyms, i.e. identical words with different meanings)
- Named entity recognition – that is, for example, distinguishing which of the occurrences of an identical word is a surname and which is a place name
This is only a short presentation of the most important techniques, proving that the topic of Natural Language Processing (NLP) is complex, which, however, translates into the fact that this technology is useful in many industries and areas. Let’s look at how you can practically take advantage of the benefits of natural language processing.
In what applications is Natural Language Processing suitable?
Natural Language Processing will work for many areas – here are some of the most interesting and popular applications.
Voicebots and chatbots
These are automatic voice or text assistants that have the ability to talk to humans in natural language. Thanks to this, the conversation resembles that conducted with a living person, and intelligent machines are able to handle virtually any matter with the client. They are used, among others, in customer service, in marketing campaigns, in online stores, in recruitment, for reservations and in many other applications.
NLP can improve search quality by better understanding and predicting what the human using the tool wants. The algorithm, constantly receiving new information based on users’ searches, can increasingly adapt to their needs. For example, it may suggest correcting a search error, adding a phrase or suggesting synonyms. This applies not only to publicly available search engines, but also to databases intended for specialists, e.g. doctors or lawyers.
Natural language Processing can be a useful way to organize digitized documents in a database – especially when there are so many of them that manual processing would be impossible. They can be searched and grouped based on an algorithm.
NLP can help analyze user opinions and comments, rating them as positive, negative and neutral, allowing you to measure sentiment in real time and respond accordingly. The algorithm can also detect specific problems reported by users, which will allow the offer and communication to be tailored to their needs. The AI algorithm also allows you to moderate content by removing comments containing prohibited words or hate speech.
Machine translation and content creation
The text is automatically translated based on millions of previous translations provided by language users. Thanks to this, the translation is fluent and takes into account linguistic nuances. Such a translation can be treated as a preliminary one and then corrected by a professional translator. Thanks to Natural Language Processing, you can also generate articles and texts, which facilitates the work of a copywriter, and also detect plagiarism in the finished text.
In what industries can Natural Language Processing be used?
It’s hard to find an industry that isn’t currently enjoying the benefits of Natural Language Processing. NLP allows you to automate many business processes that previously required tedious, manual work of people, and their implementation on such a scale was virtually impossible.
Let’s look at some of the industries that use Natural Language Processing.
In recruitment, we usually deal with a large number of CVs, which, thanks to the algorithm, can be quickly reviewed and classified into appropriate categories. Also HR voicebots may prove helpful – they will conduct an initial interview with all candidates, asking them basic questions, and screening out people who are uninterested or do not meet the main criteria. The database of candidates prepared in this way can then be dealt with by recruiters, who save months of work at the initial stage.
In this industry, we are increasingly dealing with chatbots and voicebots for customer service, which answer frequently asked questions and handle standard matters, such as recovering an account password, checking the order status or filing a complaint. Their work relieves the customer service office of repetitive tasks. Thanks to NLP, chatbots and voicebots talk to customers in natural language, i.e. similar to how a conversation with a human takes place. Voicebots are able to recognize words spoken with different accents and in different languages, so serving international customers is not a problem for them.
Healthcare systems around the world rely on electronic health records, which contain large amounts of unstructured and distributed data. Thanks to Natural Language Processing, it is possible to analyze this data and draw conclusions useful in treating patients – for example, predicting the occurrence of a disease or preventing it. Voicebots will also be useful in medical facilities, thanks to which you can make or cancel an appointment with a doctor at any time.
Lawyers often work from thousands of documents from current and past cases. Using Natural Language Processing allows them to prepare for their hearing by helping them quickly analyze data and obtain the desired information. This allows to automate the most tedious part of a lawyer’s work and save their time.
The world of finance is a place where huge amounts of data about the market and competition are created. Thanks to NLP, investors can achieve a competitive advantage by accessing information that is difficult to access and receiving its quick initial interpretations. On this basis, they can make more informed investment decisions, based on data rather than assumptions.
Online stores dominate trade, giving owners many opportunities to use technology to increase profits. Having access to millions of information about customers and their opinions, thanks to Natural Language Processing, you can analyze them and propose more personalized actions to consumers. Voicebots play an important role here – they can call the client with a personalized offer and, for example, a special discount when people are willing to abandon the cart, effectively restoring it. Natural Language Processing in ecommerce translates into greater consumer loyalty and increasing store profits.
Thanks to Natural Language Processing (NLP), you can perform accurate and user-friendly targeting based on the information we have about the behavior and preferences of each consumer. You can also use a voicebot that talks in a natural language to classify leads in an advertising campaign, calling all people immediately after they leave their contact details, which allows you to optimize the campaign in financial terms.
Challenges of Natural Language Processing
Although natural language processing techniques and algorithms are becoming better every day, there are still challenges associated with them. The first is precision – language depends on so many variables, such as slang or regional dialects or social context.
Machines are not yet always able to sense nuances and irony, but this will change over time. Another problem is that the computer does not always understand the tone of voice (which may also vary depending on the accent), which carries additional information about the message being conveyed.
It is also worth looking at the evolution of the language – the one used by people is constantly changing. The limited principles on which a computer operates may not be sufficient to understand a human being. That’s why it’s important to keep feeding the algorithms with new data. However, one issue should be noted – the algorithm, which is based on popular knowledge, often selects the most common answers from the data received, not necessarily the true ones – therefore, mechanisms should be introduced to verify the quality of artificial intelligence work.
Natural language processing – summary
Now you know exactly what NLP is. Natural Language Processing techniques benefit business and people. First of all, they facilitate communication between machines and people who do not have to operate code to perform desired tasks – this makes such cooperation much more intuitive. They work well in many industries, automating many processes and making people’s work easier. This is especially important at a time when we have more and more data and information – Natural Language Processing allows us to structure them and extract business value from them.
There are many vendors that offer trained machine learning models tailored to a variety of tasks, so every company can find the right solution for their needs. One of these solutions are voicebots – both pre-trained, and prepared for the needs of a specific business process. Find out more about how voicebot uses Natural Language Processing in many industries to support business operations.