Natural language processing – the art of interacting with robots
In July 2020, linguist Frédéric Landragin published a new book entitled “How a robot talks”, illustrating a certain fascination of the general public for the language of machines. Whether they are known from movies like R2D2 or Terminator, or as Alexa, Siri, or Google Assistant, talking robots are part of our daily lives. They are even studied in a specific field of linguistics which is called Natural language processing (NLP). This science deals with the study of algorithms that can process human language, understand it, and reproduce it.
What exactly is natural language processing?
NLP (Natural Language Processing) is a branch of artificial intelligence which deals with processing written language.
NLP focuses mainly on:
- Phonetics – the way words are pronounced in speech
- Prosody – the intonation of sentences
- Lexicon – the vocabulary that makes up a language
- Semantics and pragmatics – the meaning of a sentence and its interpretation
- Syntax and grammar
When did the concept of NLP first emerge?
The first traces of language processing appeared after the Second World War in the mid-1950s when the Americans implemented advanced analyses of Soviet communications. This was the first time that a country conducted tests to industrialize language processing.
The method is then based on an analysis of facts, rules, and the implementation of an algorithm for simulating deductive reasoning called inference engine.
The results were impressive at that time, as it was able to accurately transcribe encrypted messages. However, it did not yet allow the contextualization or the management of the ambiguous nature of a conversation, thus limiting the finesse of the analysis. We were therefore far from the era of machine learning and deep learning which strengthens NLP.
How is natural language processing powered?
There are many ways of capturing the language needed for learning:
- From a digitized text
- From a manuscript
- By voice recognition
- From extracting web pages
As we can see, any written language is of interest for NLP. Language processing methods are based on syntax, which allows us to identify the organization of components of a sentence (subject + verb + complement), and semantic analysis, which identifies the most common meaning when terms are associated. To increase the chances of accurately understanding different documents, it is necessary to have access to a large quantity of documents combined with powerful calculation tools – the famous algorithms. This combination of information and data processing is what makes it possible to train artificial intelligence efficiently. This is why the data provided by search engines like Google is extremely appealing.
Use cases of natural language processing
Helping companies interact with their customers
Chatbots, Callbots and other messaging bots are on the rise! The use of bots to prescreen incoming requests is becoming increasingly popular. These virtual conversational agents are programmed to answer the various requests of users who enter the requests on their cellphone, their computer or directly on the phone and will then converse with the bot in a natural way in writing as well as by speaking. NLP gives the bots the necessary understanding for this task. They can then answer customer requests appropriately.
This type of usage is particularly useful in sectors such as banking or insurance. Companies receive many calls on a daily basis and rely on NLP to automate first level requests.
What’s in it for the company?
- No more customers being left on hold and without an answer, which is important when you know that an unhappy customer is expensive to win back
- Enhanced processing of incoming calls and thus customers receive a quicker response
- Reduction of costs per call
- Optimal use of human resources – advisors can focus on higher value-added tasks and solutions and appreciate their work more.
Helping finance companies
Another business sector that actively uses the strengths of natural language processing is the world of finance. It is indeed a powerful tool for business intelligence. NLP’s ability to process and organize enormous amounts of data makes it a particularly effective tool for predicting market trends. Algorithms have been developed to predict the monetary direction of European central banks. This was done by analyzing the speeches of these banks’ leaders.
Don’t you think that text translation systems are getting better and better? This is thanks to the use of NLP, which automates translation based on human input. The software will analyze existing texts in two languages and understand how the content is translated from one language to the other. It considers the lexical field, the syntax, the semantics, the context – you name it.
Information is everywhere and for the last few years, social media such as Facebook, Instagram and Twitter have been committed to fighting fake news.
The first step of verification is based on an analysis of the data (number of shares, posting frequency) and its sources (type of publishing site, author, country) and then a classification of the information by degree of credibility.
As you may have understood, NLP is quite relevant for this type of task, but it still needs human intervention to complete the moderation. This is therefore an example of cooperation between humans and machines.
There are a number of companies in the reputation analysis industry. These companies are able to follow everything interesting that is said about a brand or a company in real time. Here again, the information that needs to be processed is vast and it could prove to be extremely fascinating if we add human feelings to the mix. The analysis of human behavior through writing is based on the technique of text mining or knowledge extraction.
In this case, information is extracted and sorted according to the criteria of novelty or similarity in texts written by humans. In practice, it is a matter of putting a linguistic model into an algorithm that will be compared to the real world through computerized learning and statistical systems, and natural language understanding technologies. With the help of this learning, the algorithm will be able to determine the syntactic relationship between the sentences and define the typology of the message whether it be positive, neutral, or negative.
This technique is particularly useful for companies that want to quickly understand trends in finance, food, fashion and so on.
NLP and the business world
Did you find this article interesting and are you considering using the strengths of natural language processing for your business?
If you want to:
- Improve your customer service by using chatbots or intelligent Callbots
- Optimize your customer service or contact page on your company website
- Create inclusive customer support for the visually impaired, deaf, or hard of hearing
- Provide tools capable of analyzing customer emotions such as anger, joy, and anxiety
- Aid human resources in large organizations
Get in touch with our Zaion Lab specialists! Our researchers and doctors around the world are working to accelerate the development of these revolutionary technologies on a daily basis.