Federico Scozzafava


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CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages
Tommaso Pasini | Federico Scozzafava | Bianca Scarlini
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to induce a reliable and high-coverage distribution of the meanings in a language vocabulary. To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences. Our experiments show that CluBERT learns distributions over English senses that are of higher quality than those extracted by alternative approaches. When used to induce the MFS of a lemma, CluBERT attains state-of-the-art results on the English Word Sense Disambiguation tasks and helps to improve the disambiguation performance of two off-the-shelf WSD models. Moreover, our distributions also prove to be effective in other languages, beating all their alternatives for computing the MFS on the multilingual WSD tasks. We release our sense distributions in five different languages at https://github.com/SapienzaNLP/clubert.

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Personalized PageRank with Syntagmatic Information for Multilingual Word Sense Disambiguation
Federico Scozzafava | Marco Maru | Fabrizio Brignone | Giovanni Torrisi | Roberto Navigli
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Exploiting syntagmatic information is an encouraging research focus to be pursued in an effort to close the gap between knowledge-based and supervised Word Sense Disambiguation (WSD) performance. We follow this direction in our next-generation knowledge-based WSD system, SyntagRank, which we make available via a Web interface and a RESTful API. SyntagRank leverages the disambiguated pairs of co-occurring words included in SyntagNet, a lexical-semantic combination resource, to perform state-of-the-art knowledge-based WSD in a multilingual setting. Our service provides both a user-friendly interface, available at http://syntagnet.org/, and a RESTful endpoint to query the system programmatically (accessible at http://api.syntagnet.org/).


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SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations
Marco Maru | Federico Scozzafava | Federico Martelli | Roberto Navigli
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. This paper introduces SyntagNet, a novel resource consisting of manually disambiguated lexical-semantic combinations. By capturing sense distinctions evoked by syntagmatic relations, SyntagNet enables knowledge-based WSD systems to establish a new state of the art which challenges the hitherto unrivaled performances attained by supervised approaches. To the best of our knowledge, SyntagNet is the first large-scale manually-curated resource of this kind made available to the community (at http://syntagnet.org).