Natural language processing
It is a subfield of artificial intelligence (AI) and linguistics that focuses on the interaction between computers and humans through natural language.
The goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
NLP involves a variety of tasks, including:
- Text Understanding: This involves tasks such as sentiment analysis, named entity recognition (identifying names of people, organizations, etc.), and text classification (categorizing texts into predefined categories).
- Text Generation: NLP can be used to generate human-like text, such as in language translation, summarization, and content generation.
- Speech Recognition and Synthesis: NLP techniques are also applied in speech recognition (converting spoken language into text) and speech synthesis (generating spoken language from text).
- Information Retrieval: NLP is used in search engines to understand user queries and retrieve relevant information from large datasets.
- Question Answering: NLP can be employed to build systems that can understand questions posed in natural language and provide accurate answers.
NLP systems utilize various techniques, including machine learning algorithms (such as deep learning models like recurrent neural networks and transformers), statistical methods, and linguistic rules. These techniques enable computers to analyze and understand the structure and meaning of human language, despite its inherent ambiguity and complexity.
NLP has applications in a wide range of fields, including virtual assistants, customer service automation, healthcare, finance, and more.