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A Solid Embedding Vectors Foundation Leads to Powerful NLP AI Apps

In this blog, I’ll describe how vector embeddings have emerged as a powerful tool for representing textual data within AI and natural language processing (NLP). They encode words, phrases, or entire documents into numerical vectors (floating point numbers), enabling various AI-powered tasks like text classification, name entity recognition, and semantic search. As such, embedding vectors have become the foundation for NLP AI systems, enabling machines to understand and process human language more effectively.

Embedding vectors capture the semantic meaning and relationships between words, allowing AI models to understand the context and convey richer information. They are really good at representing individual words. These word embeddings enable NLP models to comprehend similarity, analogies, and contextual associations between words, leading to improved language understanding. These embeddings capture not only the meaning of individual words but also the interplay of words within the sentence, allowing NLP models to understand and compare the meaning of whole sentences or documents.

Converting your unstructured data into vectors is not a trivial task as I’ve mentioned in my previous blog, but once you’ve done so, or “AI-ified” your data, you’re ready to reap the rewards of AI applications such as:

  1. Text Classification – Embedding vectors empower NLP models to perform accurate text classification tasks and allow use cases such as sentiment analysis, topic categorization and summary, and intent recognition.
  2. Named Entity Recognition – Embedding vectors help NLP models identify and extract named entities from text, such as people, organizations, locations, and other relevant entities. Extracting these values and other key terms from files is an important capability for quickly scanning contracts, agreements, and other legal documents to ensure compliance and avoid non-standard business dealings.
  3. Semantic Search – Embedding vectors enable an essential but often times under appreciated capability of semantic similarity search. Encoding words and sentences with vectors allows us to find and retrieve information quickly and accurately. Semantic search is also a powerful partner to generative AI, as it helps LLMs focus on the content that it needs to produce.

    It's worth taking the time to draw the relationship between semantic search and generative AI, or as we like to say at Ai Bloks, “integrating Semantic AI + Generative AI.” Generative AI does not create content on its own, at least not content that you’d used in a professional environment. You first need to connect your organizational knowledge to the AI; that’s the job of Semantic AI via embedding vectors. Only then can generative AI product content that is based on organizational data.

From the few examples of capabilities that I’ve listed above, you can see embedding vectors have transformed the landscape of NLP AI by providing a foundation for language understanding, contextual meaning, and semantic relationships. From word embeddings to sentence and document embeddings, embedding vectors empower AI models to comprehend language nuances, capture context, and generate coherent text. Before you get too ambitious with AI applications, make sure you build a strong embedding vector foundation. I will describe the top uses cases in more detail in my next blog.