Roberto Navigli
16 papers in the PaperMetrix corpus
Papers by this author
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Embeddings for Word Sense Disambiguation: An Evaluation Study
2016
Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural …
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Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
2016 · arXiv (Cornell University)
Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to …
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Just “OneSeC” for Producing Multilingual Sense-Annotated Data
2019
The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we …
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Personalized PageRank with Syntagmatic Information for Multilingual Word Sense Disambiguation
2020
Federico Scozzafava, Marco Maru, Fabrizio Brignone, Giovanni Torrisi, Roberto Navigli. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2020.
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Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation
2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A language-independent representation of meaning is one of the most coveted dreams in Natural Language Understanding. With this goal in mind, several formalisms have been proposed as frameworks for meaning representation in Semantic Parsing. And …
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AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing
2023
In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions. Our analysis reveals that the present models often violate AMR structural constraints. To address …
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Code-Switching with Word Senses for Pretraining in Neural Machine Translation
2023 · arXiv (Cornell University)
Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022). The same holds for the NMT pretraining …
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Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering
2025
One of the most widely used tasks for evaluating Large Language Models (LLMs) is Multiple-Choice Question Answering (MCQA). While open-ended question answering tasks are more challenging to evaluate, MCQA tasks are, in principle, easier to …
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SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking
2015
In this paper we present the Multilingual All-Words Sense Disambiguation and Entity Linking task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known problems in the Natural Language Processing field and both address the …
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SensEmbed: Learning Sense Embeddings for Word and Relational Similarity
2015
Ignacio Iacobacci, Mohammad Taher Pilehvar, Roberto Navigli. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.
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NASARI: a Novel Approach to a Semantically-Aware Representation of Items
2015
José Camacho-Collados, Mohammad Taher Pilehvar, Roberto Navigli. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.
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Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
2017
Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a …
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SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity
2017
This paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets …
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Neural Sequence Learning Models for Word Sense Disambiguation
2017
Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by …
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Natural Language Understanding: Instructions for (Present and Future) Use
2018
In this paper I look at Natural Language Understanding, an area of Natural Language Processing aimed at making sense of text, through the lens of a visionary future: what do we expect a machine should …
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Recent Trends in Word Sense Disambiguation: A Survey
2021
Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identifying the most suitable meaning from a predefined sense inventory. Recent breakthroughs in representation learning have fueled intensive WSD …