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.
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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. On the other hand, both traffic light and traffic signal mean the same thing. 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. […]
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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.
Copyright by morethandigital.info
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. On the other hand, both traffic light and traffic signal mean the same thing. 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:
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. […]
Read more: morethandigital.info
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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