Search Results for author: Enrico Palumbo

Found 6 papers, 1 papers with code

SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification

no code implementations SEMEVAL 2017 Rapha{\"e}l Troncy, Enrico Palumbo, Efstratios Sygkounas, Giuseppe Rizzo

In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A {``}Sentiment Analysis in Twitter{''} that aims to classify whether English tweets are of positive, neutral or negative sentiment.

General Classification Sentiment Analysis +1

Sequeval: A Framework to Assess and Benchmark Sequence-based Recommender Systems

1 code implementation11 Oct 2018 Diego Monti, Enrico Palumbo, Giuseppe Rizzo, Maurizio Morisio

In this paper, we present sequeval, a software tool capable of performing the offline evaluation of a recommender system designed to suggest a sequence of items.

Information Retrieval

Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems

no code implementations COLING 2020 Enrico Palumbo, Andrea Mezzalira, Cristina Marco, Alessandro Manzotti, Daniele Amberti

In this paper, we introduce the task of generating a test set with high semantic diversity for NLU evaluation in dialog systems and we describe an approach to address it.

Clustering Natural Language Understanding

Improving Content Retrievability in Search with Controllable Query Generation

no code implementations21 Mar 2023 Gustavo Penha, Enrico Palumbo, Maryam Aziz, Alice Wang, Hugues Bouchard

A pre-requisite to discover an entity, e. g. a book, with a search engine is that the entity is retrievable, i. e. there are queries for which the system will surface such entity in the top results.

Retrieval

Towards Graph Foundation Models for Personalization

no code implementations12 Mar 2024 Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas

In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions.

Language Modelling Large Language Model +1

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