What is NLP?
Natural Language Processing (NLP) is a technique that allows computers to comprehend words and sentences. NLP examines the grammatical structure of phrases and the particular meanings of words behind the scenes, then applies algorithms to extract meaning and produce results. In other words, it understands human language so that it can accomplish various activities automatically.
Virtual assistants like Google Assist, Siri, and Alexa are probably the most well-known instances of NLP in action. NLP recognizes written and spoken language such as “Hey Siri, where is the nearest gas station?” and converts it to numbers that machines can comprehend.
Chatbots are another well-known application of NLP. They assist support staff in resolving difficulties by automatically interpreting and responding to typical language queries.
You’ve undoubtedly come across NLP in many other programs that you use on a daily basis without even realizing it. When drafting an email, proposing to translate a Fb post written in another language, or filtering undesirable advertising emails into your junk bin, you may use text recommendations.
- The objective of Natural Language Processing is to help robots comprehend human language, which is complicated, ambiguous, and immensely diverse.
AI, ML, and NLP
Artificial Intelligence (AI), Machine Learning (ML), and Natural language processing (NLP) are all terms that are frequently used interchangeably, so it’s easy to get them mixed up.
- NLP and ML are both subcategories of AI.
Artificial intelligence (AI) is a broad phrase that refers to robots that can mimic human intelligence. Systems that simulate cognitive skills, such as learning from examples and solving problems, are included in AI. From self-driving automobiles to predictive systems, this covers a wide spectrum of applications.
Natural Language Processing (NLP) is the study of how computers comprehend and translate human speech. Machines can understand written or spoken material and execute tasks such as translation, keyword extraction, topic categorization, and more using natural language processing (NLP).
However, machine learning will be required to automate these procedures and provide reliable results. Machine learning is the process of teaching machines how to learn and develop without being explicitly programmed through the use of algorithms.
NLP and machine learning are used by AI-powered chatbots to read what users say and what they mean to accomplish, and machine learning is used to automatically offer more correct replies based on previous encounters.
NLP Techniques
Syntactic and semantic analysis are two approaches used in Natural Language Processing (NLP) to assist computers to interpret a text.
Syntactic Evaluation
Syntactic analysis, often known as parsing, examines text using fundamental grammatical principles to determine sentence structure, word organization, and word relationships.
- Tokenization is the process of breaking down a text into smaller pieces called tokens (which might be phrases or words) in order to make it easier to work with.
- Stop-word elimination eliminates often used terms that have no semantic significance.
- Tagging tokens as verbs, adverbs, adjectives, nouns, and so on is called part of speech tagging. This aids in deducing a word’s meaning.
- Lemmatization is a technique for simplifying the analysis of inflected words by reducing them to their base form.
Analytical Semantics
The goal of semantic analysis is to capture the meaning of the text. It begins by looking at the meaning of each individual word (lexical semantics). The program then examines the word combination and what it means in context.
- The goal of word sense disambiguation is to figure out which sense a word is being used in a given situation.
- Relationship extraction tries to figure out how entities (places, people, organizations, and so on) in a text relate to one another.
Conclusion
The branch of AI known as Natural Language Processing (NLP) explores how robots interact with human language. NLP is used to improve technologies that we use every day, such as chatbots, spell-checkers, and language translators.
NLP, when combined with machine learning algorithms, results in systems that learn to do tasks on their own and improve over time. Among other things, NLP-powered solutions may help you identify social media postings by emotion or extract identified entities from business correspondence.
