Remove Categorization Remove Data Extraction Remove Document
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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Businesses can now easily convert unstructured data into valuable insights, marking a significant leap forward in technology integration.

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Unlocking the Power of Data Extraction with Generative AI

TransOrg Analytics

Enter generative AI, a groundbreaking technology that transforms how we approach data extraction. Entity Recognition : Identify and categorize entities (like names, dates, or locations) within text. Summarization : Condense large documents into concise summaries, making it easier to digest extensive reports or articles quickly.

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Clinical Data Abstraction from Unstructured Documents Using NLP

John Snow Labs

Healthcare Data Abstraction: The Three Barriers To begin with, each project has its own sets of rules for what, how, and when data should be extracted and normalized. Second, the information is frequently derived from natural language documents or a combination of structured, imaging, and document sources.

NLP 52
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Intelligent Document Processing with AWS AI Services and Amazon Bedrock

ODSC - Open Data Science

Companies in sectors like healthcare, finance, legal, retail, and manufacturing frequently handle large numbers of documents as part of their day-to-day operations. These documents often contain vital information that drives timely decision-making, essential for ensuring top-tier customer satisfaction, and reduced customer churn.

IDP 98
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Streamline financial workflows with generative AI for email automation

AWS Machine Learning Blog

Many companies across all industries still rely on laborious, error-prone, manual procedures to handle documents, especially those that are sent to them by email. Intelligent automation presents a chance to revolutionize document workflows across sectors through digitization and process optimization.

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MIT Researchers Released a Robust AI Governance Tool to Define, Audit, and Manage AI Risks

Marktechpost

Although substantial research has identified and categorized these risks, a unified framework is needed to be consistent with terminology and clarity. Two taxonomies were developed: the Causal Taxonomy, categorizing risks by responsible entity, intent, and timing, and the Domain Taxonomy, classifying risks into specific domains.

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Leveraging user-generated social media content with text-mining examples

IBM Journey to AI blog

These are two common methods for text representation: Bag-of-words (BoW): BoW represents text as a collection of unique words in a text document. Term frequency-inverse document frequency (TF-IDF): TF-IDF calculates the importance of each word in a document based on its frequency or rarity across the entire dataset.