PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning

Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, Lucian Popa

AAAI 2020 pp. 13634-13635

doi:10.1609/AAAI.V34I09.7104 /aaai/2020/qian2020aaai-partner/

Abstract

Entity name disambiguation is an important task for many text-based AI tasks. Entity names usually have internal semantic structures that are useful for resolving different variations of the same entity. We present, PARTNER, a deep learning-based interactive system for entity name understanding. Powered by effective active learning and weak supervision, PARTNER can learn deep learning-based models for identifying entity name structure with low human effort. PARTNER also allows the user to design complex normalization and variant generation functions without coding skills.

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Cite

Text

Qian et al. "PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I09.7104

Markdown

[Qian et al. "PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/qian2020aaai-partner/) doi:10.1609/AAAI.V34I09.7104

BibTeX

@inproceedings{qian2020aaai-partner,
  title     = {{PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning}},
  author    = {Qian, Kun and Raman, Poornima Chozhiyath and Li, Yunyao and Popa, Lucian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13634-13635},
  doi       = {10.1609/AAAI.V34I09.7104},
  url       = {https://mlanthology.org/aaai/2020/qian2020aaai-partner/}
}