What is Natural Language Processing (NLP)?
Natural Language Processing is a branch of machine learning that enables computers to understand, interpret, and generate human language. At its core, NLP aims to bridge the gap between human communication and machine comprehension. The technology is crucial for enabling computers to understand the nuances, context, and intent behind language. NLP can power an array of applications including virtual assistants, chatbots, and sentiment analysis tools.
How Does NLP Work?
NLP models are often trained on vast datasets containing conversations, text from websites, and language patterns. With this data, NLP uses a combination of computational linguistics, machine learning, and deep learning to develop its ability to process human language.
Computational Linguistics
Computational linguistics focuses on the rules and structure of language. It analyzes syntax, morphology (word structure), and semantics to process text at a linguistic level. This involves tokenization where text is broken down into smaller units, and parsing which examines the grammatical structure of sentences. These steps help NLP systems accurately interpret complex language.
Machine Learning
Machine learning (ML) allows NLP systems to use data to improve and adapt over time. With training from vast amounts of data, NLP systems can recognize patterns, identify trends, and make predictions. For example, sentiment analysis relies heavily on ML models that are trained to classify text as positive, negative, or neutral based on the context and tone.
Deep Learning
Deep learning takes ML capabilities even further by mimicking human cognitive processes. This technology uses artificial neural networks to simulate human brain cells. Through these networks, NLP systems can identify intricate patterns in language data to make sophisticated connections while processing information in ways that closely mirror human understanding.
Benefits of NLP
Let’s examine some of the greatest benefits NLP technology can provide organizations:
Task Automation
NLP is particularly effective at automating tasks for customer service, data entry, and document management. For example, NLP-driven chatbots can understand the language in customer interactions to be able to handle standard customer inquiries without human support.
Valuable Data Insights
NLP can extract insights from unstructured data such as customer reviews or chat transcripts. This helps companies uncover trends, sentiments, and patterns to get a deeper understanding of customer preferences and pain points.
For example, eCommerce brands can use the NLP-powered sentiment analysis built into the Rep AI chat to discover common issues and reasons why visitors disengage and leave your site.
Better Discovery With Semantic Search
NLP significantly enhances on-site search functionality by interpreting the intent behind user queries, even when they include conversational language, synonyms, or typos. With semantic search, NLP understands that a query like “waterproof hiking shoes” should return results for “outdoor waterproof footwear” or “trail shoes.”
