Roberto Navigli

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Roberto Navigli is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Two is bigger (and better) than one: The wikipedia bitaxonomy project 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference English 2014 We present WiBi, an approach to the automatic creation of a bitaxonomy for Wikipedia, that is, an integrated taxonomy of Wikipage pages and categories. We leverage the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet and WikiTaxonomy. 0 0
Validating and extending semantic knowledge bases using video games with a purpose 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference English 2014 Large-scale knowledge bases are important assets in NLP. Frequently, such resources are constructed through automatic mergers of complementary resources, such as WordNet and Wikipedia. However, manually validating these resources is prohibitively expensive, even when using methods such as crowdsourcing. We propose a cost-effective method of validating and extending knowledge bases using video games with a purpose. Two video games were created to validate conceptconcept and concept-image relations. In experiments comparing with crowdsourcing, we show that video game-based validation consistently leads to higher-quality annotations, even when players are not compensated. 0 0
A quick tour of BabelNet 1.1 BabelNet
Knowledge acquisition
Multilingual ontologies
Semantic networks
Lecture Notes in Computer Science English 2013 In this paper we present BabelNet 1.1, a brand-new release of the largest "encyclopedic dictionary", obtained from the automatic integration of the most popular computational lexicon of English, i.e. WordNet, and the largest multilingual Web encyclopedia, i.e. Wikipedia. BabelNet 1.1 covers 6 languages and comes with a renewed Web interface, graph explorer and programmatic API. BabelNet is available online at http://www.babelnet.org. 0 0
Spred: Large-scale harvesting of semantic predicates ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference English 2013 We present SPred, a novel method for the creation of large repositories of semantic predicates. We start from existing collocations to form lexical predicates (e.g., break *) and learn the semantic classes that best fit the * argument. To do this, we extract all the occurrences in Wikipedia which match the predicate and abstract its arguments to general semantic classes (e.g., break Body Part, break Agreement, etc.). Our experiments show that we are able to create a large collection of semantic predicates from the Oxford Advanced Learner's Dictionary with high precision and recall, and perform well against the most similar approach. 0 0
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network Graph algorithms
Knowledge acquisition
Semantic networks
Word sense disambiguation
Artificial Intelligence English 2012 We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks. © 2012 Elsevier B.V. 0 0
WiSeNet: Building a Wikipedia-based semantic network with ontologized relations Information extraction
Knowledge acquisition
Relation ontologization
Semantic network
ACM International Conference Proceeding Series English 2012 In this paper we present an approach for building a Wikipedia-based semantic network by integrating Open Information Extraction with Knowledge Acquisition techniques. Our algorithm extracts relation instances from Wikipedia page bodies and ontologizes them by, first, creating sets of synonymous relational phrases, called relation synsets, second, assigning semantic classes to the arguments of these relation synsets and, third, disambiguating the initial relation instances with relation synsets. As a result we obtain WiSeNet, a Wikipedia-based Semantic Network with Wikipedia pages as concepts and labeled, ontologized relations between them. 0 0
Two Birds with One Stone: Learning Semantic Models for Text Categorization and Word Sense Disambiguation Word Sense Disambiguation
Text Classification
Text Categorization
Wikipedia
Proceedings of the 20th ACM International Conference on Information and Knowledge Management 2011 In this paper we present a novel approach to learning semantic models for multiple domains, which we use to categorize Wikipedia pages and to perform domain Word Sense Disambiguation (WSD). In order to learn a semantic model for each domain we first extract relevant terms from the texts in the domain and then use these terms to initialize a random walk over the WordNet graph. Given an input text, we check the semantic models, choose the appropriate domain for that text and use the best-matching model to perform WSD. Our results show considerable improvements on text categorization and domain WSD tasks. 0 0
Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation Text classification
Word sense disambiguation
International Conference on Information and Knowledge Management, Proceedings English 2011 In this paper we present a novel approach to learning semantic models for multiple domains, which we use to categorize Wikipedia pages and to perform domain Word Sense Disambiguation (WSD). In order to learn a semantic model for each domain we first extract relevant terms from the texts in the domain and then use these terms to initialize a random walk over the WordNet graph. Given an input text, we check the semantic models, choose the appropriate domain for that text and use the best-matching model to perform WSD. Our results show considerable improvements on text categorization and domain WSD tasks. 0 0
BabelNet: Building a very large multilingual semantic network ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference English 2010 In this paper we present BabelNet - a very large, wide-coverage multilingual semantic network. The resource is automatically constructed by means of a methodology that integrates lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition Machine Translation is also applied to enrich the resource with lexical information for all languages. We conduct experiments on new and existing gold-standard datasets to show the high quality and coverage of the resource. 0 0
Knowledge-rich Word Sense Disambiguation rivaling supervised systems ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference English 2010 One of the main obstacles to high-performance Word Sense Disambiguation (WSD) is the knowledge acquisition bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of semantic relations from an encyclopedic resource, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, simple knowledge-lean disambiguation algorithms compete with state-of-the-art supervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets. 0 0
Learning Word-Class Lattices for definition and hypernym extraction ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference English 2010 Definition extraction is the task of automatically identifying definitional sentences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current approaches - mostly focused on lexicosyntactic patterns - suffer from both low recall and precision, as definitional sentences occur in highly variable syntactic structures. In this paper, we propose Word- Class Lattices (WCLs), a generalization of word lattices that we use to model textual definitions. Lattices are learned from a dataset of definitions from Wikipedia. Our method is applied to the task of definition and hypernym extraction and compares favorably to other pattern generalization methods proposed in the literature. 0 0
Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia Wikipedia
Knowledge acquisition
Taxonomy learning
Category disambiguation
Word sense disambiguation
IJCAI 2009: 21st International Joint Conference on Artificial Intelligence, Pasadena, California 2009 We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet. 0 0
Large-scale taxonomy mapping for restructuring and integrating Wikipedia IJCAI International Joint Conference on Artificial Intelligence English 2009 We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet. 0 0
Large-scale taxonomy mapping for restructuring and integrating wikipedia IJCAI English 2009 0 0