Search Results for author: Giuseppe Rizzo

Found 17 papers, 7 papers with code

Benchmarking the Extraction and Disambiguation of Named Entities on the Semantic Web

no code implementations LREC 2014 Giuseppe Rizzo, Marieke van Erp, Rapha{\"e}l Troncy

Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as those in DBpedia, has been tackled by the Semantic Web community.

Benchmarking Entity Linking +3

Analysis of Named Entity Recognition and Linking for Tweets

no code implementations27 Oct 2014 Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, Kalina Bontcheva

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area.

Entity Disambiguation Language Identification +4

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

A Distributed and Accountable Approach to Offline Recommender Systems Evaluation

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

For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion.

Information Retrieval

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

A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

no code implementations13 Nov 2018 Martino Mensio, Emanuele Bastianelli, Ilaria Tiddi, Giuseppe Rizzo

As the first robotic platforms slowly approach our everyday life, we can imagine a near future where service robots will be easily accessible by non-expert users through vocal interfaces.

Natural Language Understanding Semantic Parsing

All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation

no code implementations2 Sep 2019 Diego Monti, Giuseppe Rizzo, Maurizio Morisio

The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems.

Clustering Recommendation Systems

Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion

1 code implementation8 Feb 2020 Andrea Fiandro, Giorgio Crepaldi, Diego Monti, Giuseppe Rizzo, Maurizio Morisio

This paper describes the solution of the POLINKS team to the RecSys Challenge 2019 that focuses on the task of predicting the last click-out in a session-based interaction.

Ensemble Learning

A Response Retrieval Approach for Dialogue Using a Multi-Attentive Transformer

1 code implementation15 Dec 2020 Matteo A. Senese, Alberto Benincasa, Barbara Caputo, Giuseppe Rizzo

Our approach makes use of a neural architecture based on transformer with a multi-attentive structure that conditions the response of the agent on the request made by the user and on the product the user is referring to.

Retrieval

Attention-based Clinical Note Summarization

1 code implementation18 Apr 2021 Neel Kanwal, Giuseppe Rizzo

In recent years, the trend of deploying digital systems in numerous industries has hiked.

Clinical Information Retreival Extractive Summarization

Cannot find the paper you are looking for? You can Submit a new open access paper.