Search Results for author: Stefano Faralli

Found 23 papers, 2 papers with code

Large-scale Taxonomy Induction Using Entity and Word Embeddings

no code implementations4 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.

Word Embeddings

ECIR 2020 Workshops: Assessing the Impact of Going Online

no code implementations14 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.

14

Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph

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.

Knowledge Graphs

Multiple Knowledge GraphDB (MKGDB)

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.

Knowledge Graphs

Enriching Frame Representations with Distributionally Induced Senses

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.

Frame

A Framework for Enriching Lexical Semantic Resources with Distributional Semantics

no code implementations23 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.

Word Sense Disambiguation

Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

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.

Open Information Extraction Question Answering +1

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

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.

Word Sense Disambiguation

Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation

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.

Word Embeddings Word Sense Induction

Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation

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.

Machine Translation Translation +2

A Large DataBase of Hypernymy Relations Extracted from the Web.

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.

Word Sense Disambiguation

A New Method for Evaluating Automatically Learned Terminological Taxonomies

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.

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