Information Extraction to Identify Novel Technologies and Trends in Renewable Energy

Résumé

Achieving carbon neutrality by 2050 requires unprecedented technological,economic, and sociological changes. With time as a scarce resource, it iscrucial to base decisions on relevant facts and information to avoid misdirection.This study aims to help decision makers quickly find relevant information relatedto companies and organizations in the renewable energy sector. Over the courseof this PhD program, we will propose several text-mining methods applied tothe renewable energy sector in order to detect technological breakthroughs andnew, innovative companies. These techniques include specialized Named EntityRecognition (NER) models, news summarization, and trend analysis of scientificarticles. Further steps in this project will contain a TRIZ-based analysis of scientificarticles in order to attribute a multi-factor score on the innovative potential ofnovel technologies.

BibTeX

@inproceedings{maclean:hal-04703230,
  TITLE = {{Information Extraction to Identify Novel Technologies and Trends in Renewable Energy}},
  AUTHOR = {Maclean, Connor and Cavallucci, Denis},
  URL = {https://hal.science/hal-04703230},
  NOTE = {Part 3: Interdisciplinary and Cognitive Approaches in TRIZ},
  BOOKTITLE = {{World Conference of AI-Powered Innovation and Inventive Design 24th IFIP WG 5.4 International TRIZ Future Conference, TFC 2024, Cluj-Napoca, Romania, November 6--8, 2024, Proceedings}},
  ADDRESS = {Cluj-Napoca, Romania},
  EDITOR = {Denis Cavallucci and Stelian Brad and Pavel Livotov},
  PUBLISHER = {{Springer Nature Switzerland}},
  SERIES = {IFIP Advances in Information and Communication Technology},
  VOLUME = {AICT-736},
  YEAR = {2024},
  MONTH = Nov,
  KEYWORDS = {Natural Language Processing ; Named Entity Recognition ; Topic Modeling ; Renewable Energy ; TRIZ},
  PDF = {https://hal.science/hal-04703230v1/file/638204_1_En_22_Chapter_OnlinePDF.pdf},
  HAL_ID = {hal-04703230},
  HAL_VERSION = {v1},
}