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UID:pretalx-pycones-2024-K3HKMM@pretalx.com
DTSTART;TZID=CET:20241006T111500
DTEND;TZID=CET:20241006T115000
DESCRIPTION:In the evolving landscape of corporate reporting\, processing l
 arge amounts of unstructured data is crucial to aligning companies' Enviro
 nmental\, Social\, and Governance (ESG) strategies with the challenges bro
 ught by climate change and social injustice. The latest European sustainab
 ility reporting directive (CSRD) requires companies to disclose clearly th
 eir impacts\, risks\, and opportunities in terms of ESG. \nUsing a variety
  of NLP techniques\, enhanced by state-of-the-art text generation models\,
  we will show how to automatically scan through thousands of reports and e
 xtract relevant information\, best practices\, and potential risks or oppo
 rtunities.\nThis session will illustrate a real-world case study on the ES
 G landscape using different Natural Language Processing (NLP) techniques. 
 By leveraging zero-shot Text classification\, sentiment analysis\, Named E
 ntity Recognition (NER)\, clustering on top of matryoshka embeddings and T
 opic Modelling\, we can improve the insights and decision-making. In addit
 ion\, we will present how to take advantage of the latest advances in Gene
 rative AI.\nThis presentation will be particularly valuable for data scien
 tists\, ESG analysts\, and Python enthusiasts interested in the intersecti
 on of AI and sustainable business practices. The main takeaways  that you 
 will get from this presentation include:\n- Gaining insights into how topi
 c modelling with Bertopic can be used in corporate ESG initiatives.\n- Dis
 covering how clustering techniques help tailor insights about Impact\, Ris
 ks and Opportunities from large ESG reports.\n- Understanding the role of 
 matryoshka embeddings for enhancing the latency when processing ESG-relate
 d texts.\n- Understanding the role of Generative AI in extracting actionab
 le insights from vast amounts of ESG data.
DTSTAMP:20260721T034736Z
LOCATION:Orballo
SUMMARY:Navigating the ESG landscape with LLMs - Martín Quesada Zaragoza\,
  Vincent Rizzo
URL:https://pretalx.com/pycones-2024/talk/K3HKMM/
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