32 citations · 91 across the 34 of their papers we have counts for
9 papers · 1 filter
Relation Clustering in Narrative Knowledge Graphs
Simone Mellace, K Vani, Alessandro Antonucci
When coping with literary texts such as novels or short stories, the extraction of structured information in the form of a knowledge graph might be hindered by the huge number of p…
Causal Expectation-Maximisation
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of caus…
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
K Vani, Sandra Mitrovic, Alessandro Antonucci +1
Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift traci…
Tractable Inference in Credal Sentential Decision Diagrams
Lilith Mattei, Alessandro Antonucci, Denis Deratani Mauá +2
Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. They allow for a compact representation of…
Structural Causal Models Are (Solvable by) Credal Networks
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of…
Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks
Alessandro Antonucci, Gastone Pietro Rosati Papini, Luigi Palopoli +1
Generating accurate and efficient predictions for the motion of the humans present in the scene is key to the development of effective motion planning algorithms for robots moving…
Temporal Embeddings and Transformer Models for Narrative Text Understanding
Vani K, Simone Mellace, Alessandro Antonucci
We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word…
A Bayesian Approach to Conversational Recommendation Systems
Francesca Mangili, Denis Broggini, Alessandro Antonucci +2
We present a conversational recommendation system based on a Bayesian approach. A probability mass function over the items is updated after any interaction with the user, with info…
Approximate MMAP by Marginal Search
Alessandro Antonucci, Thomas Tiotto
We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models. The algorithm is based on a reduction of the task to a polynomial number of marginal inference…