NLP explained – What is Natural Language Processing?
Purpose of Natural Language Processing (NLP) and corresponding examples of applications.
Natural Language Processing (NLP for short) links the machine processing of natural language in computer science. Based on the natural and written or spoken language, the computer can analyze, understand and process the language of humans.
The goal of NLP is to understand natural language with the help of algorithms and rules and also to generate it oneself. For this purpose, knowledge from computer science and linguistics are combined. As a result, NLP is a type of artificial intelligence, which has numerous areas of application, especially in the field of companies and unstructured data. Areas of application include communication between humans and machines in the form of, for example, enterprise search engines or chatbots.
The focus here is on understanding the information not only on the basis of keywords, but in its entire semantic context. NLP is then able to correctly interpret and interpret context-dependent text contexts. The challenge here lies in the complexity of human language. Some words have different meanings depending on the situation and social context. But on the other hands sometimes we have more words for the same meaning. An understanding of such differences is therefore elementary in NLP. Since computers, in contrast to humans, cannot rely on experience for a better understanding of language, various algorithms and methods of machine learning are used. NLP consists of representing this information in a numerical format that can be understood by the computer.
Initially, therefore, an NLP application requires the use of large amounts of data for learning different patterns and for sense analysis. However, this does not always have to be internal company data, but can in many cases also be freely accessible data on the Internet. Companies do not have to provide data or server capacities for an NLP application per se.
Different fields of NLP
The recognition of human speech can be divided into different areas. These represent different steps, which are subsequently used for the overall recognition of the text:
- Recognition of the language
- Classification of individual words and sentences
- Acquisition of grammatical information such as basic forms
- Identifying individual word functions in a sentence (subject, verb, object, adjective, etc.)
- Interpretation of the meaning of (partial) sentences
- Comprehending sentence contexts and relationships
Enormous developments in the field of NLP have enormously increased the application possibilities and scalability for e.g. enterprise search engines. Nevertheless, NLP currently still reaches its limits in the interpretation of certain stylistic devices (rhetorical questions, irony or paradoxes).
Classic NLP application areas
1. Question-Answer models
Problems of this type are about answering questions with as precise an answer as possible. The more concrete the answer should be, the more complex the task is for the computer. The easiest way to do this is, for example, the complete extraction of a text passage; alternatively, one could also extract concrete words or package them in answer sentences. The next level would be logical inference from textual information. For example, the text could contain the information that employees A, B and C are located in the PR department. The logical answer to the question “How many employees does our PR department have?” would then be three.
A medium-sized company has various data silos with different information and documents. An intelligent enterprise search engine can answer questions like “How do I resolve error code #err49284?”.
A corporation always receives the same customer inquiries. Here, the company can use a chatbot to answer the customer’s questions automatically.
2. Classification of different sequences
The goal of this scope is to classify text into predefined classes. A predefined class could be, for example, emotions such as happy, sad or angry. The computer decides independently to which class the text presented to it is assigned. Just as well, texts could be assigned to authors or formats of articles (blog, opinion, news). So the length of the texts can be chosen arbitrarily.
A comparison portal wants to sort negative reviews by content. Existing customer reviews with a negative rating are thus divided into, for example, the classes “Complaints about customer service”, “Complaints about user-friendliness” and “Complaints about prices”. Each negative rating (e.g. with 3 stars or worse) is now assigned to a class.
A machine builder receives mail addressed to different departments. Instead of selecting them manually, NLP can be used to subdivide them into delivery bills, invoices, and other requests.
3. Generating texts
Based on given texts, suitable words should be suggested to complete the text. This in turn can be used for further text prediction and completion.
The developer of a document management program wants to simplify document finding similar to the enterprise search engine provider. To do this, he predicts the potential search queries in the search mask with matching words.
4. Sentence element identification
This NLP area deals with the identification of different sentence elements such as subjects, predicates or objects. Alternatively, these can also be natural persons, companies, times or e-mail addresses.
A company uses an enterprise search engine to extract from minutes the contact persons and deadlines of the respective subjects.
The computer’s task is to change longer texts into shorter ones, taking into account grammatical rules. In the process, the content must not change, so important and unimportant information must be recognized.
A publisher wants to automatically summarize longer articles for an online short version. For this purpose, it has a short summary created in length and language complexity depending on the user profile.
Texts are translated into several languages in compliance with the applicable spelling rules. Both the content and the grammar of the original text must be kept close.
A mechanical engineering manufacturer wants to expand from the DACH region into the international arena and therefore needs to translate all product descriptions and technical specifications into other languages. The challenge is additional technical and industry-specific vocabulary.
This list can also be extended to include other use cases such as speech-to-text conversions or speech recognition.
How will NLP develop in the future?
As one of the most promising forms of artificial intelligence, research is currently being conducted most intensively in the field of NLP. Rapid developments in recent years, which above all enable a more resource-efficient development, promise equally rapid developments in the future.
The use of NLP is already no longer reserved for large corporations, but is becoming accessible to everyone through translation or search tools. Future developments will bring further use cases and even further reduced costs.
It will be interesting to see how AI will develop in this area.
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