8 min read
Natural Language Processing (NLP) is a crucial topic within the vast field of artificial intelligence (AI). But what does NLP mean exactly and why is its development important for business?
- Definition: Natural Language Processing (NLP)
- Application areas in the field of NLP
- The biggest NLP trends in everyday business life
DEfinition: Natural Language Processing (NLP)
Back to start of the page
NLP is part of linguistics, computer science and artificial intelligence and develops linguistic interactions of computers and humans. It particularly focuses on how computers can be programmed to process large amounts of natural language and ideally also understand the content, including contextual features such as the use of certain terms, metaphors or even tonality.
Some of the biggest challenges and topics are speech recognition, language comprehension, and natural language generation (NLG), i.e., the processing and communicating of data in a language form that humans can understand.
NLP is not an invention of the 21st century, in fact, first experiments were set up in the 1950s, mainly dealing with translation as well as the development of human-like interactions with computers. If you want to read a bit more about this, I recommend the simulation ELIZA (YouTube video), which was able to generate partially human-like responses to statements like "My head hurts" as early as 1966.
As with all megatrends, every enterprise and organization needs to evaluate the technology before it adapts it. It's very likely that not every single industry, market or business model is in dire need of NLP-supported technology to invest in. It is always best to determine whether there are use cases that support business goals, e.g., optimize processes, reduce costs, increase revenue, create new business models, etc.
With the development of Machine Learning and especially Deep Learning, NLP has spread and developed rapidly compared to previous decades, covering numerous areas and use cases.
Application areas in the field of NLP
Back to start of the page
Optical Character Recognition
The recognition of text based on an image with text on it.
The recognition and correct capture of spoken language (also: speech-to-text).
The conversion of written text into speech. This is currently used in particular for visually impaired people but is also heavily used on social media platforms such as TikTok.
Named Entity Recognition (NER)
The recognition of names and the identification of the type of name (place, person, object, etc.).
The identification of a sentiment towards certain topics/statements. This is used, for example, in social media monitoring to identify how people feel about a brand (positive or negative).
The evaluation of relationships between named people, objects, etc. E.g., to find out if people in a text are related.
The identification and structuring of an argument in a text, e.g., to concentrate the premise, arguments and conclusion.
The logical and comprehensible summarization of a text.
Conducting conversations with people from a computer.
Making it easier for users to pull specific data from different documents without having AI expertise. So, these computers are interfaces between AI applications and users to facilitate interaction between users and AI.
The purely machine translation of text from one language to another. Popular examples include DeepL or Google Translate.
Natural Language Generation
The machine generation of language that humans can also understand in terms of content (see above).
Natural Language Understanding (NLU)
The machine understanding of human language, both acoustically, grammatically and semantically. This includes converting text modules into structures that the computer can understand, so that they can be processed more easily.
The reading and meaningful answering of posed closed and open-ended questions (written or spoken). Especially answering open questions, i.e., questions that do not have a correct answer, is very complex.
The biggest NLP trends in everyday business life
Back to start of the page
With this many areas (and many more) developing NLP technology to further expand how computers identify and understand language and react to it, there are many use cases for businesses that already can be implemented.
Chatbots to offer support for users both for text and.
Speech recognition is particularly exciting when it comes to accents, dialects and other variations in pronunciation.
Without AI, both the development and use of chatbots would be severely limited and incredibly regimented. AI, meanwhile, allows chatbots to learn what users mean based on their interactions without, for example, phrasing a question exactly as it is specified in the program. As a result, chatbots can now have more realistic "conversations" and users can communicate more easily. In addition, individual characteristics of the chatbot can be generated more and more frequently, for example through humorous answers or particularly friendly phrasing. In fact, many chatbots nowadays have designers who work on a "chatbot personality" that fits the brand.
Reading out emotions and / or moods from speech, whether on social media, in chat conversations, or even on the phone.
Natural Language Processing is still in its infancy here in some cases, as ambiguities, irony, sarcasm and other stylistic means are difficult to identify (even for humans). Nevertheless, companies can already use these tools to document brand perceptions on social networks, for example.
Additionally, these forms of speech recognition can be used for chatbots to identify potentially escalating customer interactions early on and bring in a service representative to support and solve the issue.
Translations are already supported by AI in many areas.
DeepL and Google Translate are popular examples of this, which are available for free and, precisely because of this, learn faster than translation software with a smaller user group. After all, the more comparable data is available, the easier it is for artificial intelligence to learn from it and implement it.
Especially the extraction of linguistic peculiarities, ambiguities as well as stylistic idiosyncrasies still poses a challenge.
However, it is astonishing how good translations are these days, especially in "popular" languages such as English or Chinese. Professional translators might not have to fear for their jobs in the next years, since translation tools usually need quality assurance, especially when it comes to more creative texts or less known languages, but these tools do make work easier, especially in an environment that uses standardized texts such as law, business, etc.
There are about five languages in which NLP models are mainly developed. As a result, many of the exciting and helpful applications are often only available in English and/or Chinese, for example. So, while English-speaking companies can already take advantage of a wide range of smart applications, companies in other languages often have develop their own solutions or resort to more traditional processes (e.g., translators).
In recent years, this need has increased the focus on NLP models that can be used without English as the base language and can process numerous other languages at the same time. In the next few years, this will ensure an extreme acceleration in the application of chatbots, language assistants, translation tools, etc. in the European area, among others.
While NLP itself is a complex discipline that requires a lot of expertise, there are now an increasing number of tools that enable the application of NLP through simple modules or building block models. This helps users without programming or machine learning knowledge to set up (simple) applications and allows more companies to test and apply NLP technology which in turn promotes and accelerates the development of these technologies.
At DIGITALL, our experts deal with a wide array of AI technology and help you develop both the data foundations to follow your AI goals as well as develop solutions and integrate the right platforms for you.