A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text
A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text
Andrea Seveso, Fabio Mercorio, Mario Mezzanzanica
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 4917-4918.
https://doi.org/10.24963/ijcai.2021/690
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.
Keywords:
Natural Language Processing: Knowledge Extraction
Data Mining: Recommender Systems
Humans and AI: Human-AI Collaboration
Knowledge Representation and Reasoning: Semantic Web