no code implementations • LREC 2022 • Stefano Faralli, Andrea Lenzi, Paola Velardi
Knowledge is the lifeblood for a plethora of applications such as search, recommender systems and natural language understanding.
no code implementations • LREC 2022 • Riccardo Orlando, Simone Conia, Stefano Faralli, Roberto Navigli
In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing.
no code implementations • 4 May 2021 • Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto, Heiko Paulheim
Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies.
no code implementations • 14 May 2020 • Sérgio Nunes, Suzanne Little, Sumit Bhatia, Ludovico Boratto, Guillaume Cabanac, Ricardo Campos, Francisco M. Couto, Stefano Faralli, Ingo Frommholz, Adam Jatowt, Alípio Jorge, Mirko Marras, Philipp Mayr, Giovanni Stilo
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
no code implementations • LREC 2020 • Georgeta Bordea, Stefano Faralli, Fleur Mougin, Paul Buitelaar, Gayo Diallo
In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies.
no code implementations • LREC 2020 • Stefano Faralli, Paola Velardi, Farid Yusifli
MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others.
no code implementations • LREC 2018 • Stefano Faralli, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto
We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora.
no code implementations • 23 Dec 2017 • Chris Biemann, Stefano Faralli, Alexander Panchenko, Simone Paolo Ponzetto
While both kinds of semantic resources are available with high lexical coverage, our aligned resource combines the domain specificity and availability of contextual information from distributional models with the conciseness and high quality of manually crafted lexical networks.
1 code implementation • LREC 2018 • Alexander Panchenko, Dmitry Ustalov, Stefano Faralli, Simone P. Ponzetto, Chris Biemann
In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms.
no code implementations • LREC 2018 • Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann
We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7. 5 billion of named entity occurrences in 14. 3 billion sentences from a web-scale crawl of the \textsc{Common Crawl} project.
1 code implementation • EMNLP 2017 • Alexander Panchenko, Fide Marten, Eugen Ruppert, Stefano Faralli, Dmitry Ustalov, Simone Paolo Ponzetto, Chris Biemann
In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.
no code implementations • WS 2017 • Alex Panchenko, er, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed.
no code implementations • EACL 2017 • Stefano Faralli, Alex Panchenko, er, Chris Biemann, Simone Paolo Ponzetto
In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies.
no code implementations • EACL 2017 • Alex Panchenko, er, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann
On the example of word sense induction and disambiguation (WSID), we show that it is possible to develop an interpretable model that matches the state-of-the-art models in accuracy.
no code implementations • SEMEVAL 2016 • Alex Panchenko, er, Stefano Faralli, Eugen Ruppert, Steffen Remus, Hubert Naets, C{\'e}drick Fairon, Simone Paolo Ponzetto, Chris Biemann
no code implementations • LREC 2016 • Julian Seitner, Christian Bizer, Kai Eckert, Stefano Faralli, Robert Meusel, Heiko Paulheim, Simone Paolo Ponzetto
Hypernymy relations (those where an hyponym term shares a {``}isa{''} relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e. g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction.
no code implementations • LREC 2012 • Paola Velardi, Roberto Navigli, Stefano Faralli, Juana Maria Ruiz Martinez
Our method assigns a similarity value B{\textasciicircum}i{\_}(l, r) to the learned (l) and reference (r) taxonomy for each cut i of the corresponding anonymised hierarchies, starting from the topmost nodes down to the leaf concepts.