Paola Velardi

Also published as: P. Velardi


2020

pdf bib
Multiple Knowledge GraphDB (MKGDB)
Stefano Faralli | Paola Velardi | Farid Yusifli
Proceedings of the 12th Language Resources and Evaluation Conference

We present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy. 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. Our resource consists of a large hypernymy graph which counts more than 37 million nodes and more than 81 million hypernymy relations.

2018

pdf bib
A Large Multilingual and Multi-domain Dataset for Recommender Systems
Giorgia Di Tommaso | Stefano Faralli | Paola Velardi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

pdf bib
Hashtag Sense Clustering Based on Temporal Similarity
Giovanni Stilo | Paola Velardi
Computational Linguistics, Volume 43, Issue 1 - April 2017

Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion. Regardless of the use for which they were originally intended, hashtags cannot be used as a means to cluster messages with similar content. First, because hashtags are created in a spontaneous and highly dynamic way by users in multiple languages, the same topic can be associated with different hashtags, and conversely, the same hashtag may refer to different topics in different time periods. Second, contrary to common words, hashtag disambiguation is complicated by the fact that no sense catalogs (e.g., Wikipedia or WordNet) are available; and, furthermore, hashtag labels are difficult to analyze, as they often consist of acronyms, concatenated words, and so forth. A common way to determine the meaning of hashtags has been to analyze their context, but, as we have just pointed out, hashtags can have multiple and variable meanings. In this article, we propose a temporal sense clustering algorithm based on the idea that semantically related hashtags have similar and synchronous usage patterns.

pdf bib
What to Write? A topic recommender for journalists
Alessandro Cucchiarelli | Christian Morbidoni | Giovanni Stilo | Paola Velardi
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter andWikipedia, either not covered or poorly covered in the published news articles.

2013

pdf bib
Automated learning of everyday patients’ language for medical blogs analytics
Giovanni Stilo | Moreno De Vincenzi | Alberto E. Tozzi | Paola Velardi
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

pdf bib
OntoLearn Reloaded: A Graph-Based Algorithm for Taxonomy Induction
Paola Velardi | Stefano Faralli | Roberto Navigli
Computational Linguistics, Volume 39, Issue 3 - September 2013

2012

pdf bib
A New Method for Evaluating Automatically Learned Terminological Taxonomies
Paola Velardi | Roberto Navigli | Stefano Faralli | Juana Maria Ruiz Martinez
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Abstract Evaluating a taxonomy learned automatically against an existing gold standard is a very complex problem, because differences stem from the number, label, depth and ordering of the taxonomy nodes. In this paper we propose casting the problem as one of comparing two hierarchical clusters. To this end we defined a variation of the Fowlkes and Mallows measure (Fowlkes and Mallows, 1983). Our method assigns a similarity value B^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. For each cut i, the two hierarchies can be seen as two clusterings C^i_l , C^i_r of the leaf concepts. We assign a prize to early similarity values, i.e. when concepts are clustered in a similar way down to the lowest taxonomy levels (close to the leaf nodes). We apply our method to the evaluation of the taxonomy learning methods put forward by Navigli et al. (2011) and Kozareva and Hovy (2010).

2010

pdf bib
Learning Word-Class Lattices for Definition and Hypernym Extraction
Roberto Navigli | Paola Velardi
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

pdf bib
An Annotated Dataset for Extracting Definitions and Hypernyms from the Web
Roberto Navigli | Paola Velardi | Juana Maria Ruiz-Martínez
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper presents and analyzes an annotated corpus of definitions, created to train an algorithm for the automatic extraction of definitions and hypernyms from web documents. As an additional resource, we also include a corpus of non-definitions with syntactic patterns similar to those of definition sentences, e.g.: ""An android is a robot"" vs. ""Snowcap is unmistakable"". Domain and style independence is obtained thanks to the annotation of a large and domain-balanced corpus and to a novel pattern generalization algorithm based on word-class lattices (WCL). A lattice is a directed acyclic graph (DAG), a subclass of nondeterministic finite state automata (NFA). The lattice structure has the purpose of preserving the salient differences among distinct sequences, while eliminating redundant information. The WCL algorithm will be integrated into an improved version of the GlossExtractor Web application (Velardi et al., 2008). This paper is mostly concerned with a description of the corpus, the annotation strategy, and a linguistic analysis of the data. A summary of the WCL algorithm is also provided for the sake of completeness.

2006

pdf bib
Enriching a Formal Ontology with a Thesaurus: an Application in the Cultural Heritage Domain
Roberto Navigli | Paola Velardi
Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge

2004

pdf bib
Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation
Roberto Navigli | Paola Velardi
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

pdf bib
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Roberto Navigli | Paola Velardi
Computational Linguistics, Volume 30, Number 2, June 2004

pdf bib
Automatic Generation of Glosses in the OntoLearn System
Alessandro Cucchiarelli | Roberto Navigli | Francesca Neri | Paola Velardi
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

pdf bib
Quantitative and Qualitative Evaluation of the OntoLearn Ontology Learning System
Roberto Navigli | Paola Velardi | Alessandro Cucchiarelli | Francesca Neri
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2002

pdf bib
Automatic Adaptation of WordNet to Domains
Roberto Navigli | Paola Velardi
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

pdf bib
Identification of Relevant Terms to Support the Construction of Domain Ontologies
Paola Velardi | Michele Missikoff | Roberto Basili
Proceedings of the ACL 2001 Workshop on Human Language Technology and Knowledge Management

pdf bib
Unsupervised Named Entity Recognition Using Syntactic and Semantic Contextual Evidence
Alessandro Cucchiarelli | Paola Velardi
Computational Linguistics, Volume 27, Number 1, March 2001

2000

pdf bib
Will Very Large Corpora Play For Semantic Disambiguation The Role That Massive Computing Power Is Playing For Other AI-Hard Problems?
Alessandro Cucchiarelli | Enrico Faggioli | Paola Velardi
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

pdf bib
Dependency of context-based Word Sense Disambiguation from representation and domain complexity
Paola Velardi | Roma Velardi
NAACL-ANLP 2000 Workshop: Syntactic and Semantic Complexity in Natural Language Processing Systems

1998

pdf bib
Automatic Semantic Tagging of Unknown Proper Names
Alessandro Cucchiarelli | Danilo Luzi | Paola Velardi
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

pdf bib
Automatic Semantic Tagging of Unknown Proper Names
Alessandro Cucchiarelli | Danilo Luzi | Paola Velardi
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

pdf bib
Automatic Adaptation of WordNet to Sublanguages and to Computational Tasks
Roberto Basili | Alessandro Cucchiarelli | Carlo Consoli | Maria Teresa Pazienza | Paola Velardi
Usage of WordNet in Natural Language Processing Systems

1997

pdf bib
Automatic Selection of Class Labels from a Thesaurus for an Effective Semantic Tagging of Corpora.
Alessandro Cucchiarelli | Paola Velardi
Fifth Conference on Applied Natural Language Processing

1996

pdf bib
Unsupervised Learning of Syntactic Knowledge: Methods and Measures
R. Basili | A. Marziali | M.T. Pazienza | P. Velardi
Conference on Empirical Methods in Natural Language Processing

pdf bib
Integrating General-purpose and Corpus-based Verb Classification
Roberto Basili | Maria Teresa Pazienza | Paola Velardi
Computational Linguistics, Volume 22, Number 4, December 1996

1994

pdf bib
A “not-so-shallow” parser for collocational analysis
R. Basili | M.T. Pazienza | P. Velardi
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

pdf bib
The Noisy Channel and the Braying Donkey
Roberto Basili | Maria Teresa Pazienza | Paola Velardi
The Balancing Act: Combining Symbolic and Statistical Approaches to Language

1993

pdf bib
Hierarchical Clustering of Verbs
Roberto Basili | Maria Pazienza | Paola Velardi
Acquisition of Lexical Knowledge from Text

1992

pdf bib
Computational Lexicons: the Neat Examples and the Odd Exemplars
Roberto Basili | Maria Teresa Pazienza | Paola Velardi
Third Conference on Applied Natural Language Processing

1991

pdf bib
How to Encode Semantic Knowledge: A Method for Meaning Representation and Computer-Aided Acquisition
Paola Velardi | Maria Teresa Pazienze | Michela Fasolo
Computational Linguistics, Volume 17, Number 2, June 1991

1990

pdf bib
Why Human Translators Still Sleep in Peace? (Four Engineering and Linguistic Gaps in Nlp)
Paola Velardi
COLING 1990 Volume 2: Papers presented to the 13th International Conference on Computational Linguistics

1989

pdf bib
Computer Aided Interpretation of Lexical Cooccurrences
Paola Velardi | Maria Teresa Pazienza
27th Annual Meeting of the Association for Computational Linguistics

1987

pdf bib
A Structured Representation of Word-Senses for Semantic Analysis.
Maria Teresa Pazienza | Paola Velardi
Third Conference of the European Chapter of the Association for Computational Linguistics