A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text
Abstract
Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.
Cite
Text
Seveso et al. "A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/690Markdown
[Seveso et al. "A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/seveso2021ijcai-human/) doi:10.24963/IJCAI.2021/690BibTeX
@inproceedings{seveso2021ijcai-human,
title = {{A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text}},
author = {Seveso, Andrea and Mercorio, Fabio and Mezzanzanica, Mario},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4917-4918},
doi = {10.24963/IJCAI.2021/690},
url = {https://mlanthology.org/ijcai/2021/seveso2021ijcai-human/}
}