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.

Seveso, A., Mercorio, F., Mezzanzanica, M. (2021). A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp.4917-4918). International Joint Conferences on Artificial Intelligence.

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

Seveso A.
;
Mercorio F.;Mezzanzanica M.
2021

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.
paper
human-ai teaming, machine learning, taxonomy enrichment, word-embeddings
English
30th International Joint Conference on Artificial Intelligence, IJCAI 2021
2021
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
9780999241196
2021
4917
4918
none
Seveso, A., Mercorio, F., Mezzanzanica, M. (2021). A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp.4917-4918). International Joint Conferences on Artificial Intelligence.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/362351
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