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.
No
paper
Scientifica
human-ai teaming, machine learning, taxonomy enrichment, word-embeddings
English
30th International Joint Conference on Artificial Intelligence, IJCAI 2021
9780999241196
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.
Seveso, A; Mercorio, F; Mezzanzanica, M
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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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