Word2vec: What Are Word Embeddings? A Complete Guider
By Pushpam Punjabi
In our ongoing series of blogs “Unravelling the AI mystery” Digitate continues to explore advances in AI and our experiences in turning AI and GenAI theory into practice. The blogs are intended to enlighten you as well as provide perspective into how Digitate solutions are built.
Please enjoy the blogs
2. Prompt Engineering – Enabling Large Language Models to Communicate With Humans
3. What are Large Language Models? Use Cases & Applications
4. Harnessing the power of word embeddings
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Word2vec: What Are Word Embeddings? A Complete Guide
Humans have a very intuitive way of working with languages. Tasks such as understanding similar texts, translating a text, completing a text, and summarizing a text come very naturally to humans with an inherent understanding of language semantics. But when it comes to computers, passing on this intuition is an uphill task! Sure, computers can assess how structurally similar two strings are. When you type “Backstreet Boys,” a computer might correct you to “Backstreet Boys,” but how do you make them understand the semantics of words?
- How do you make a computer infer that king and queen carry the same equivalence as man and woman?
- How do you make a computer infer that in a conversation about technology companies, the term Apple refers to the company and not the fruit?
- How do you make a computer infer that if someone is searching for football legends and has searched Ronaldo, they might (should!) also be interested in Messi?
- How do you make a computer recommend “GoodFellas” or “The Irishman” when someone has browsed for “The Godfather”?
- How do you accomplish this mammoth task of bridging the gap between humans and computers to infer the capacity to interpret languages? The answer to these questions lies in this tutorial on the concept of “word embeddings”! Read on!