Teaching computers to understand, interpret, and generate human language with the nuance and context of a native speaker.
Natural Language Processing (NLP) is a branch of artificial intelligence that gives computers the ability to understand text and spoken words in much the same way human beings can.
It combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
The building blocks that allow machines to decode the complexity of human communication.
Breaking down text into smaller units like words or phrases to make it easier for machines to analyze.
Identifying the emotional tone behind a body of text to understand attitudes, opinions, and emotions.
Locating and classifying key information in text into predefined categories such as names, places, and dates.
The automated process of translating text or speech from one natural language to another using software.
From the phone in your pocket to enterprise-scale analytics, NLP is everywhere.
Customer service agents that understand intent and provide 24/7 support.
Converting speech to text and executing commands in real-time.
Distilling long articles into key bullet points and summaries.
Comprehensive guides and case studies to accelerate your productivity.
The industry standards for building modern language applications.
Industrial-Strength NLP
Natural Language Toolkit
Transformer Models
Large Language Models
Bias is a critical challenge. Modern NLP research focuses on using diverse training datasets, implementing debiasing algorithms, and conducting rigorous red-teaming to ensure models provide fair and equitable responses.
Resource distribution is uneven. While English has massive datasets, many "low-resource" languages lack the data needed for high-quality models. Cross-lingual transfer learning is a key area of study to bridge this gap.
Privacy-preserving techniques like differential privacy and federated learning allow models to learn from sensitive data without exposing individual user information or storing personal identifiers.
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