If you think that you have never encountered NLP, just open Google, click on the microphone icon next to the search bar, and say: “Ok, Google ...”, and the search engine processes the request you need.
However, this function would not be available if the device hadn’t been able to understand the natural language that people speak. The ability of a machine to process what was said, to structure the received information, to determine the necessary response and respond in a language that the user understands is NLP or Natural Language Processing.
How Natural Language Processing Works
The process of machine understanding using NLP algorithms can look as follows:
- A person says something to the machine.
- The machine records sound.
- The audio is being converted into text.
- The NLP system parses the text into components, understands the context of the conversation, and the intention of the person.
- Based on the results of the NLP, the machine determines which command should be executed.
On the one hand, everything looks quite simple. However, human speech is significantly different from the speech of a device or a robot. The main difficulty for developers is that the machine takes everything too literally, and the natural language is vibrant and full of polysemantic words, homonyms, and often has hidden subtexts that not everyone captures. There is also a concept of an ellipsis, when we omit the repeating words to avoid tautology, for example: “I like Siri, and he likes Alexa”. Also, no one has canceled the emergence of new words.
All these nuances create confusion in “understanding.” Therefore, no matter how simple the tasks may seem, their solution is somewhat complicated, because, for a machine, our language is not natural.
Tasks that NLP can Solve
Whatever we write or say, we receive a text as an output, which is the processable object for NLP. Thus, we learn to carry out operations that can be performed with the text by using a machine. NLP covers a quite wide range of tasks, and natural language processing examples are:
- Machine translation.
- Spelling and grammar check.
- Speech recognition and question-answering.
- Voice control.
- Summarization (the process of searching for the important points and creating the text summary).
- Sentiment analysis.
- Text categorization.
- Display of relevant online ads (search for a similar context).
New tasks emerge every day, and because NLP is so multitasking and can process a significant amount of data, it is being used in many fields of activity.
The Main Areas of Application of Speech Recognition Technologies
Sentiment analysis and selection of advertising
Sentiment analysis is trendy among people in business, marketers, and politicians. Due to the constant increase in the amount of information, the previously familiar technologies become less effective. The ability to quickly monitor and control public opinion is still the key to success.
Classical polls have long faded into the background. Even those who want to support brands or political candidates are not always ready to spend time filling out questionnaires. However, people willingly share their opinions on social networks. The search for negative texts and the identification of the main complaints significantly helps to change concepts, improve products and advertising, as well as reduce the level of dissatisfaction. In turn, explicit positive reviews increase ratings and demand.
Marketers also use NLP to search for people with a likely or explicit intention to make a purchase. Behavior on the Internet, maintaining pages on social networks, and queries to search engines provide a lot of useful unstructured customer data. Selling the right ad for internet users allows Google to make the most of its revenue. Advertisers pay Google every time a visitor clicks on an ad. A click can cost anywhere from a few cents to more than $ 50.
NLP has become the basis for creating chatbots. We wrote about them earlier. However, it should be added that natural language processing chatbots help solve the problem of call centers’ and reception departments’ workloads. For example, after Kyivstar introduced the Zoriana chatbot, the load of operators decreased significantly. Thanks to an extensive base of 12,000 standardized answers, the bot helps with solving 70% of incoming questions. This confirms the effectiveness of chatbots use for large companies.
Unexpected Use of Natural Language Processing Capabilities
Many organizations need NLP as a data structuring helper. The goal of digitizing information is still valid today, and we again turn to the natural language processor, which analyzes the documentation and classifies it.
NLP is also used in medicine to improve patient care, maintain medical records, and search for key terms in the professional literature. With its help, robotic doctors are implemented that compare the patients’ symptoms with appropriate diagnoses and monitor the course of the disease.
Furthermore, data processing and forecasting capabilities enable the use of natural language processing applications for crime prevention. By using them, the police can analyze criminal activity, figure out the code words of criminals in advertising, and react faster to avoid violence and human trafficking. And this is probably among the most impressive uses of natural language processing to date.
How NLP Will Change in the Nearest Future
It is undeniable that new technologies are way more effective than old ones. Therefore, the popularity of natural language processing projects is only growing. According to the global forecast of a leading research company in the B2B sector, MarketAndMarkets, the market for NLP-based software products, which today stands at about $ 7.5 billion, will grow to $ 16 billion by 2021. Many areas of human activity can no longer do without speech recognition technology due to the massive amount of unstructured data.
Our specialists at Evergreen are experienced in creating NLP-based products. Send us a message, and we will help you realize a project, e.g., a chatbot, which requires high-quality recognition of human speech.