Word embeddings have revolutionized the field of Natural Language Processing (NLP) in recent years, providing a powerful and flexible way to represent the meaning of words in a form that computers can process. These dense vector representations serve as the foundation for virtually all modern NLP applications, from simple sentiment analysis to complex language generation…
Tokenization is a foundational step in nearly all NLP pipelines. It‘s the process of splitting text into smaller units called tokens that can then be fed into a language model or other NLP system. The tokens could be individual characters, subwords, or whole words. The goal is to represent text in a way that‘s optimized…
If you‘re working on any kind of natural language processing application, chances are you‘ve heard of spaCy. spaCy is a powerful open-source library for advanced NLP in Python. It offers a concise API for common NLP tasks like tokenization, part-of-speech tagging, named entity recognition, and more. One of spaCy‘s biggest advantages is its extensive collection…
Language is at the heart of human intelligence and communication. The ability to express complex thoughts, share knowledge, and connect with others through a rich system of spoken and written symbols is a hallmark of our species. As computing has advanced, a key goal of artificial intelligence has been to imbue machines with this quintessential…
In the era of big data, unstructured text comprises a significant portion of the information generated every day. From social media posts and customer reviews to news articles and medical records, text data holds immense value for businesses and researchers alike. However, making sense of this vast amount of textual information poses a challenge. This…
Tokenization is a fundamental preprocessing step in natural language processing (NLP) that splits text into smaller units called tokens. It is a critical task that directly impacts the performance of downstream NLP applications. Over the years, tokenization techniques have evolved significantly – from simple rule-based methods to advanced subword tokenization algorithms like byte pair encoding…