Language Processing is a technology used by computers to understand a person's natural language and behavior.
NLP draws on many subjects, including computer science and computer computing, analysis, deception, in its attempt to close the gap between human communication and computer understanding.
1. Mobile applications that translate language or web applications such as Google Translate.
2. Grammar Word Processors tools such as Microsoft text and Word and Grammarly software use NLP to check the accuracy of the text.
3.Personal services such as Google-based Google, Apple-based Siri, Cortana, and Alexa-based Amazon.
Statistical analysis used in NLP
Syntactic analysis and semantic analysis are 2 key techniques used to work in the Natural Language Processing algorithms.
1. Syntax - In NLP, the syntax analysis process is used to define a natural language that complies with the rules of grammar and prediction.
2. Semantics - Semantics refers to the meaning defined by a text. Semantic analysis is one of the techniques/methods for Natural Language Processing.
The flow of basic NLP performance in Humar's communications
The basic flow between human-machine communication using Indigenous Language processing can go like this:
1. Someone is talking to NLP-based programs.
2. NLP programs capture audio.
3. The computer uses a neural network model to perform speech recognition that converts native language into machine language.
4. After that program changes and the program analyzes the code and applies it as a result.
5. An NLP-based machine responds to a person by playing an audio file
Other open-source NLP Source libraries for application development
These open-source libraries provide AI algorithmic building blocks for NLP code in applications. This open-source algorithm provides free NLP encoding blocks for the provision of servers and infrastructure.
1. Apache OpenNLP - a machine learning algorithm that provides tokens, sentence classification.
2. Natural Language Toolkit (NLTK) - Python library that provides text processing, classification modules.
3. Stanford NLP - an integrated NLP toolkit.
4. MALLET - a Java-based package for natural language processing, modeling, networks.
AI-based Natural Language Processing algorithms play an important role in building machine-to-human communication systems.