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Andrea Cimino

dblp:146/3795 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0003-0900-473XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3Databases, data management, data science and information retrieval · 2Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 50% Machine translation · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Machine translation › parallel corpora
parallel corpus construction
0.212016
PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification · EMNLP 2016
Natural language and speech › Language models and text generation › text generation
text simplification
0.212016
PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification · EMNLP 2016

Methods — techniques the papers use, named apart from their topics

automatic corpus acquisition · 0.2
YearPublicationVenuePosition
2020 Profiling-UD: a Tool for Linguistic Profiling of Texts
abstract
In this paper, we introduce Profiling–UD, a new text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives. It allows the extraction of more than 130 features, spanning across different levels of linguistic description. Beyond the large number of features that can be monitored, a main novelty of Profiling–UD is that it has been specifically devised to be multilingual since it is based on the Universal Dependencies framework. In the second part of the paper, we demonstrate the effectiveness of these features in a number of theoretical and applicative studies in which they were successfully used for text and author profiling.
Dominique Brunato, Andrea Cimino, Felice Dell'Orletta, Giulia Venturi, Simonetta Montemagni
LREC2
2018 Real-World Witness Detection in Social Media via Hybrid Crowdsensing
Stefano Cresci, Andrea Cimino, Marco Avvenuti, Maurizio Tesconi, Felice Dell'Orletta
ICWSM2
2016 PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification
abstract
In this paper we present PaCCSS-IT, a Parallel Corpus of Complex-Simple Sentences for ITalian.To build the resource we develop a new method for automatically acquiring a corpus of complex-simple paired sentences able to intercept structural transformations and particularly suitable for text simplification.The method requires a wide amount of texts that can be easily extracted from the web making it suitable also for less-resourced languages.We test it on the Italian language making available the biggest Italian corpus for automatic text simplification.
Dominique Brunato, Andrea Cimino, Felice Dell'Orletta, Giulia Venturi
EMNLP2
2015 Crisis Mapping During Natural Disasters via Text Analysis of Social Media Messages
abstract
Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed.
Stefano Cresci, Andrea Cimino, Felice Dell'Orletta, Maurizio Tesconi
WISE (2)2
2014 T2K^2: a System for Automatically Extracting and Organizing Knowledge from Texts
Felice Dell'Orletta, Giulia Venturi, Andrea Cimino, Simonetta Montemagni
LREC3