This article discusses the history and current applications of natural language processing (NLP). NLP is used to apply computational techniques to text or speech, in order to derive structure or meaning. This can involve sentiment analysis, topic identification, language detection, key phrase extraction, and other text analytics tasks.
NLP: Subfield of linguistics, computer science, and AI
Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
NLP is used to apply computational techniques to text or speech, in order to derive structure or meaning. This can involve sentiment analysis, topic identification, language detection, key phrase extraction, and other text analytics tasks. In addition, NLP can be used to build language models, which can be used to generate text or speech.
The early days of NLP
The history of natural language processing (NLP) dates back to the 1950s, when a group of researchers at Georgetown University first applied computational methods to the analysis of human language. Since then, NLP has grown into a thriving field of research, with applications in a variety of domains such as machine translation, information retrieval, and question answering.
In the early days of NLP, much of the research was focused on developing algorithms for automatic language understanding and generation. One of the first successes in this area was the ELIZA program, developed in 1966 by Joseph Weizenbaum. ELIZA was designed to simulate a human therapist, and was quite successful at fooling people into thinking they were talking to a real person.
Since then, considerable progress has been made in developing NLP systems that can carry out more complex tasks such as machine translation and question answering. These systems are based on a variety of techniques, including statistical methods, rule-based systems, and neural networks.
Current applications of NLP
With the rapid growth of data available in digital form, NLP is now being applied to a variety of tasks such as text summarization, opinion mining, and entity recognition. As the field continues to evolve, we can expect to see even more exciting applications of NLP in the future.
Some of the current applications of Natural language processing are
- NLP for virtual assistants like Alexa, Siri, and Google assistant.
- Sentiment analysis to understand the opinion of the user from the text.
- Chatbots to have a conversation with the user.
- Machine translation to translate the text from one language to the another language.
In conclusion, natural language processing is a rapidly growing field with a wide range of applications. From virtual assistants to sentiment analysis, NLP is being used in a variety of ways to make our lives easier. As the field continues to evolve, we can expect to see even more exciting applications of NLP in the future.