Navid Nobani

According to our database, Navid Nobani authored at least 11 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

ConvXAI: a System for Multimodal Interaction with Any Black-box Explainer.

Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani

Cogn. Comput., March, 2023

A survey on XAI and natural language explanations.

Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani

Inf. Process. Manag., 2023

2022

ContrXT: Generating contrastive explanations from any text classifier.

Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani, Andrea Seveso

Inf. Fusion, 2022

Embeddings Evaluation Using a Novel Measure of Semantic Similarity.

Anna Giabelli, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani

Cogn. Comput., 2022

The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers.

Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani, Andrea Seveso

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

MEET-LM: A method for embeddings evaluation for taxonomic data in the labour market.

Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani

Comput. Ind., 2021

TaxoRef: Embeddings Evaluation for AI-driven Taxonomy Refinement.

Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Towards an Explainer-agnostic Conversational XAI.

Navid Nobani, Fabio Mercorio, Mario Mezzanzanica

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

GRASP: Graph-based Mining of Scientific Papers.

Navid Nobani, Mauro Pelucchi, Matteo Perico, Andrea Scrivanti, Alessandro Vaccarino

Proceedings of the 10th International Conference on Data Science, 2021

A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract).

Navid Nobani, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

MEET: A Method for Embeddings Evaluation for Taxonomic Data.

Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani

Proceedings of the 20th International Conference on Data Mining Workshops, 2020