A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text

Andrea Seveso; Fabio Mercorio; Mario Mezzanzanica

IJCAI 2021poster1 citations

A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text

Andrea Seveso, Fabio Mercorio, Mario Mezzanzanica

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.

Natural Language Processing: Knowledge ExtractionData Mining: Recommender SystemsHumans and AI: Human-AI CollaborationKnowledge Representation and Reasoning: Semantic Web

BibTeX

@inproceedings{ijcai2021p690,
  title     = {A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text},
  author    = {Seveso, Andrea and Mercorio, Fabio and Mezzanzanica, Mario},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4917--4918},
  year      = {2021},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2021/690},
  url       = {https://doi.org/10.24963/ijcai.2021/690},
}