Meaning and applications
Natural Language Processing (NLP) is a branch of computer science that aims to make written and spoken language understandable to computers. The language that computers understand best is code, but unfortunately humans don’t communicate in code. Well, sometimes maybe ;), but humans prefer natural language. NLP, according to Gartner, is “the natural language processing technology capable of transforming text or speech into coded, structured information based on an appropriate ontology.”
In this article, we’ll take a look at what exactly Natural Language Processing means and the opportunities it offers businesses.
Machine learning is the brain behind NLP
Writing rules in code for every possible combination of words in any language to help machines understand language is a hopeless task. That’s why NLP combines computational linguistics – rule-based modeling of human language – with statistical analysis based on machine learning and deep learning models. These statistical models are used to provide the best possible approximation of the speaker’s or author’s actual meaning, intent, and mood based on statistical assumptions.
Machine learning relies heavily on data to make these assumptions. Without data, artificial intelligence cannot learn. Therefore, to train an NLP algorithm, a corpus of text or spoken language is needed.
Applications of Natural Language Processing
NLP drives well-known computer programs, such as translation programs like Google Translate or Deepl, voice assistants like Siri, Alexa or Google’s Assistant, or chatbots like Amazon’s Lex-Bot or Maggi’s Kim. But there are also lesser-known applications that rely on NLP. In healthcare, NLP technology is used to generate insights from past patient data. Unstructured healthcare data can be precisely organized with NLP to generate insights for patient treatment or improve predictive analytics about patient health.
The independent research organization TNO uses NLP to filter relevant content from data and classify it by topic. This information, automatically extracted from unstructured text, is used to identify trends or sentiment analysis.
What NLP can be used for:
Automatic translation from one language to another
Summarizing text (useful for extracting relevant text from large studies, for example)
Speech recognition or transcription of spoken from speech to text (text to speech)
Formulation of written text into spoken language
Sentiment analysis – how positive or negative the speech is
Text classification – assigning predefined categories to text documents
Question and answer – understanding the meaning of questions and giving answers
Query analysis and content analysis – determining a person’s intentions and needs when interacting with a machine (chatbot, search engine, voice assistant)
Spam detection (detecting words, grammatical errors).
Difference between NLP, NLU and NLG
Natural Language Processing does not stand alone, but encompasses several applications, including Natural Language Understanding (NLU) and Natural Language Generation (NLG). These three techniques go hand in hand.
Natural Language Understanding
While NLP is mainly concerned with converting unstructured language input into structured data, NLU is concerned with interpreting and understanding language. Grammar and context are also taken into account so that the speaker’s intent is also clear. NLU uses AI (artificial intelligence) algorithms to do this. These algorithms use a mixture of linguistic, rule-based, and pure systems to process and translate natural language correctly in terms of content. For example, they can perform statistical analysis and then identify similarities in text that has not yet been analyzed.
People say or write the same things in different ways, make spelling mistakes, use incomplete sentences, or the wrong words when searching for something in a search engine. With NLU, computer applications can infer intent from speech, even if the written or spoken language is incorrect. NLP looks at what was said, and NLU looks at what was meant.
Natural Language Generation
NLP is the generation of text based on structured data. NLP can therefore be used the other way around, by giving the initiative to communicate to the computer rather than the human. For example, NLP can generate content-based briefings and indicate what content should be covered when writing about a particular topic. This can even be implemented at different expert levels or for different phases of the sales funnel.