In the age of artificial intelligence, natural language processing is becoming more and more popular, even though computational linguistics is a field several decades older. What is natural language processing (NLP) and how is it applied in business and daily life? What is the precise mechanism of natural language processing technology? Continue reading to find out in a moment.
Natural Language Processing (NLP): What is it?
What is meant by NLP? In a nutshell, natural language processing is the capacity of a computer software to comprehend spoken or written human language. This idea comes from the artificial intelligence domain.
With natural language processing, computers can produce natural language as well as interpret it (natural language generation, or NLG).
Many of the technologies and solutions used in business and daily life today are built around this technology. Examples include ChatGPT, spam filtering, an internet search engine that suggests related terms, automatic text translation, grammar checking, and text and voice virtual assistants like chatbots and voicebots that deal with customers talking to them (as well as assistants that “live” in our phones, like Siri or Alexa).
NLP is also employed in email, where it offers a recommendation for a reply based on the content of the message. Numerous businesses possess databases containing unstructured textual data that can solely be efficiently handled using Natural Language Processing.
We shall go into greater detail regarding the applications of natural language processing (NLP) in various business domains and industries later on.
Let’s first discuss the operation of the technology that underpins NLP.
What is the process of Natural Language Processing?
Natural language is understood by computers thanks to NLP, exactly like by people. Text readers and microphones are used to gather input (much how people use their senses). The computer-understandable code is then created from the input.
Creation of Techniques for Natural Language Processing
The Turing test, which was created to determine whether a computer might be genuinely intelligent, marked the beginning of natural language processing history in the 1950s. The test’s success depended on the computer’s ability to comprehend and produce natural language.
Prior to the 1990s, computers could comprehend the structure and meaning of human language when they adhered to a set of linguistic principles that formed the basis of natural language processing.
This strategy started to give way in the 1990s to machine learning algorithms, which make use of statistical techniques. They develop their ability to carry out tasks based on the training data they are given, and they adjust their procedures in response to fresh data, making predictions based on the generalization of the provided examples.
This type of system makes use of deep learning and so-called neural networks, which allow it to function somewhat like the human brain. Additionally, the algorithms are able to learn and become more efficient by looking for patterns and regularities in the data.
It is crucial that there be an adequate amount of linguistic data, though; one way to achieve this is by using a search engine that users from all over the world frequently use to submit questions. An example pertaining to sentiment analysis is also worthwhile to provide; the algorithm is trained to identify words that possess a good, negative, or neutral nature. It then uses this information to categorize more samples.
Natural language processing has been more and more popular since 2000. In addition to scientific advancements, computer science has gained several real-world uses. Classical linguistics and statistical methods are used in NLP research nowadays, with the latter becoming more and more prevalent.
Huge volumes of data are currently used to train neural networks, which may cause issues with excessive load. However, there is a workaround: the algorithm first learns on a large set of data and is subsequently fine-tuned based on a smaller set, closely connected to the task at hand.
Techniques for Natural Language Processing
Natural language needs to be broken up into components that computers can comprehend in order for it to be processed. In order for the machine to evaluate the text data—or voice that has been translated to text—you must first pre-prepare and “clean” it, making it simpler so that the computer can interpret it. After that, the language is processed once more utilizing new methods and developed algorithms.
First, tokenization is used; in this instance, the text is divided into smaller sections, or what are known as tokens, which are words, their components (prefixes, suffixes), or word sequences. These are often recurring patterns that function as building blocks for additional processing.
Moreover, stop word elimination is used, which eliminates tokens that will be rejected by processing. Some words, like “that” or “and,” are brief and commonly used but lack significant meaning. Although this speeds up processing, it’s not a commonly utilized strategy because it can overlook small meaning variations.
Additionally, “bag of words” models should be mentioned here because, although being unordered sets of tokens, they can be a practical, easy-to-use tool for preliminary analysis of vast volumes of data.
Human language can be processed through the application of sophisticated linguistic techniques known as natural language processing. Semantic analysis and syntactic analysis are the two primary categories of approaches.
The process of organizing words into a phrase that makes grammatical sense is known as syntax. Among the techniques that are available are:
Parsing is the study of a sentence’s grammar.
Word segmentation, or the examination of the text’s word division
Sentence splitting, or the division of the text into separate sentences using, say, dots
Morphological segmentation is the process of breaking words down into smaller units called morphemes.
By breaking down words into their most fundamental forms and putting them together, stemming and lemmatization help you organize the text even more effectively.
Part-of-speech tagging, or labeling words with the part of speech they represent (noun, verb, adjective, etc.),
Conversely, methods of semantic analysis concentrate on the meaning of words in their context. They are still in the development stage because of how complicated they are. As an illustration, consider this:
Disambiguating a word’s meaning based on its context of use is known as word sense disambiguation (with regard to homonyms, i.e. identical words with distinct meanings)
Named entity recognition is the process of identifying, for instance, which use of an identical term is a geographical name and which is a surname.
This is but a brief overview of the key methods, demonstrating the complexity of the field of natural language processing (NLP) and the wide range of applications and fields in which it finds use. Let’s examine how you can use natural language processing to your practical advantage.
What uses is natural language processing appropriate for?
The following are some of the more intriguing and well-liked uses for natural language processing.
Chatbots and voicebots
These are text- or voice-activated robots that can converse in natural language with people. As a result, the dialogue is more like having a chat with a live person, and sophisticated algorithms can manage almost any issue involving the client. Customer service, marketing campaigns, online retailers, hiring, reservations, and a host of other uses all make use of them.
Lookup engines
By better understanding and forecasting what a human user of the tool wants, NLP can enhance the quality of searches. The algorithm can more and more adjust to the wants of users since it is continuously gaining new data from their searches. For instance, it can advise adding a word, proposing synonyms, or fixing a search engine error. This goes for both search engines that are accessible to the general public and databases designed for experts, like physicians or attorneys.
Arranging papers
When there are so many digital documents in a database that human processing is impractical, natural language processing (NLP) can be a helpful organizing tool. They can be sorted using an algorithm and searched.
Social networks
NLP may assist in the analysis of user comments and opinions, classifying them as neutral, negative, or positive. This enables you to assess sentiment in real time and adjust your response appropriately. Additionally, the system can identify certain issues that customers have identified, enabling the offer and communication to be customized to meet their needs. You can also use the AI system to regulate material by eliminating remarks that use hate speech or phrases that are prohibited.
Content generation and machine translation
The machine translation of the text makes use of millions of prior translations submitted by language users. This allows the translation to flow naturally and cater for language quirks. Such a translation can be considered preliminary, and an expert translator can then revise it. Natural language processing also makes it possible to detect plagiarism in the final document and to generate texts and articles, which helps copywriters in their work.
Which sectors can make use of natural language processing?
It’s difficult to think of a sector of the economy that doesn’t presently profit from natural language processing. Numerous commercial procedures that previously needed laborious, manual labor from individuals and were nearly impossible to conduct on such a large scale can now be automated thanks to natural language processing (NLP).
Let’s examine a few sectors that employ natural language processing.
Personnel
During the hiring process, we often deal with a significant volume of CVs, which the algorithm makes it easy to swiftly examine and classify into the relevant categories. Additionally, HR voicebots could be useful. They will interview all candidates initially, asking standard questions and eliminating those who don’t fit the core requirements or are indifferent. Recruiters may then handle the database of candidates created in this manner, saving months of labor in the beginning.
Client support
In this sector, customer support chatbots and voicebots—which respond to often requested questions and manage routine tasks like retrieving account passwords, tracking orders, or registering complaints—are becoming more and more common. Their labor relieves the customer service department of tedious duties. Thanks to natural language processing (NLP), chatbots and voicebots may converse with clients in a manner that is akin to having a human-to-human discussion. Voicebots can easily serve consumers from around the world since they can detect words spoken in a variety of languages and dialects.
Medical Care
Electronic health records are the foundation of healthcare systems worldwide, and they hold vast quantities of dispersed, unstructured data. Natural language processing makes it feasible to evaluate this data and derive conclusions that are helpful in patient treatment, such as anticipating or averting the development of a disease. Voicebots will be helpful in medical facilities as well, allowing you to schedule or cancel doctor appointments whenever you’d like.
Law
Attorneys frequently use thousands of papers from cases both past and present. They can get ready for their hearing by using Natural Language Processing, which enables them to swiftly evaluate data and extract the needed information. This enables time-saving automation of the most laborious aspect of a lawyer’s job.
Money
Large volumes of data about the market and competitors are generated in the finance industry. Investors can get a competitive edge by using natural language processing (NLP) to quickly understand hard-to-get information and obtain first interpretations. They are then in a better position to make data-driven investment decisions as opposed to just speculative ones.
Online shopping
Trade is dominated by online retailers, who have several chances to leverage technology to boost sales. With Natural Language Processing, you may analyze and suggest more customized actions to customers based on millions of data about them and their thoughts. Voicebots are crucial in this situation since they can efficiently restore the cart by calling the customer and making a unique offer, such as a great price, in exchange for their willingness to relinquish it. In e-commerce, natural language processing leads to higher customer loyalty and higher store earnings.
Promoting
With the help of Natural Language Processing (NLP), you may target customers with accuracy and ease by using the data we have about their interests and behavior. Additionally, you may optimize the campaign financially by using a voicebot that speaks in natural language to categorize leads in an advertising campaign and call every individual as soon as they provide their contact information.
Natural Language Processing’s Difficulties
Even while algorithms and approaches for natural language processing are improving daily, there are still issues with them. The first is accuracy; there are a lot of factors that affect language, like slang, regional dialects, and social context.
Irony and subtleties are still above the current capabilities of machines, but this will change in the future. The computer’s inability to comprehend speech tonality—which can also differ based on accent—which conveys more information about the message being sent—is another issue.
It’s also important to consider how language has evolved; people’s language usage is ever-evolving. It’s possible that a computer’s narrow operating principles are insufficient to comprehend a human. It’s crucial to continuously provide fresh data to the algorithms because of this. It is important to keep in mind that the algorithm, which relies on popular knowledge, frequently chooses the most popular responses from the data it receives rather than the ones that are accurate. As a result, systems for confirming the accuracy of artificial intelligence work should be implemented.
Overview of natural language processing
You are now fully aware of what NLP is. Both people and businesses can profit from natural language processing tools. They make cooperation much more natural by first facilitating communication between people and machines. This eliminates the need for code to be operated in order to accomplish desired tasks. They are effective across a wide range of sectors, streamlining workflows and automating numerous tasks. With the abundance of data and information available today, Natural Language Processing plays a critical role in organizing and gaining business value from them.
Every business can discover the ideal solution for its needs from a wide range of vendors who offer trained machine learning models suited to a variety of jobs. Voicebots are one of these solutions; they are pre-programmed and taught to meet the demands of particular business processes. Learn more about the various industries that voicebot supports with Natural Language Processing and how it helps with business operations.