Fabio Massimo Zanzotto

Also published as: F. Zanzotto, Fabio Zanzotto, Fabio Massimo Zanzotto


2020

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KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations
Fabio Massimo Zanzotto | Andrea Santilli | Leonardo Ranaldi | Dario Onorati | Pierfrancesco Tommasino | Francesca Fallucchi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Syntactic parsers have dominated natural language understanding for decades. Yet, their syntactic interpretations are losing centrality in downstream tasks due to the success of large-scale textual representation learners. In this paper, we propose KERMIT (Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees) to embed symbolic syntactic parse trees into artificial neural networks and to visualize how syntax is used in inference. We experimented with KERMIT paired with two state-of-the-art transformer-based universal sentence encoders (BERT and XLNet) and we showed that KERMIT can indeed boost their performance by effectively embedding human-coded universal syntactic representations in neural networks

2018

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SyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons
Fabio Massimo Zanzotto | Andrea Santilli
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper, we present SyntNN as a way to include traditional syntactic models in multilayer neural networks used in the task of Semeval Task 2 of emoji prediction. The model builds on the distributed tree embedder also known as distributed tree kernel. Initial results are extremely encouraging but additional analysis is needed to overcome the problem of overfitting.

2015

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Squibs: When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics
Fabio Massimo Zanzotto | Lorenzo Ferrone | Marco Baroni
Computational Linguistics, Volume 41, Issue 1 - March 2015

2014

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Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels
Nghia The Pham | Lorenzo Ferrone | Fabio Massimo Zanzotto
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTE
Lorenzo Ferrone | Fabio Massimo Zanzotto
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Towards Syntax-aware Compositional Distributional Semantic Models
Lorenzo Ferrone | Fabio Massimo Zanzotto
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Transducing Sentences to Syntactic Feature Vectors: an Alternative Way to “Parse”?
Fabio Massimo Zanzotto | Lorenzo Dell’Arciprete
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality

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Linear Compositional Distributional Semantics and Structural Kernels
Lorenzo Ferrone | Fabio Massimo Zanzotto
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

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SemEval-2013 Task 5: Evaluating Phrasal Semantics
Ioannis Korkontzelos | Torsten Zesch | Fabio Massimo Zanzotto | Chris Biemann
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2011

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Linguistic Redundancy in Twitter
Fabio Massimo Zanzotto | Marco Pennacchiotti | Kostas Tsioutsiouliklis
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Irina Matveeva | Alessandro Moschitti | Lluís Màrquez | Fabio Massimo Zanzotto
Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing

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Distributed Structures and Distributional Meaning
Fabio Massimo Zanzotto | Lorenzo Dell’Arciprete
Proceedings of the Workshop on Distributional Semantics and Compositionality

2010

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Syntactic/Semantic Structures for Textual Entailment Recognition
Yashar Mehdad | Alessandro Moschitti | Fabio Massimo Zanzotto
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing
Carmen Banea | Alessandro Moschitti | Swapna Somasundaran | Fabio Massimo Zanzotto
Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing

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Expanding textual entailment corpora fromWikipedia using co-training
Fabio Massimo Zanzotto | Marco Pennacchiotti
Proceedings of the 2nd Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources

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Estimating Linear Models for Compositional Distributional Semantics
Fabio Massimo Zanzotto | Ioannis Korkontzelos | Francesca Fallucchi | Suresh Manandhar
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Generic Ontology Learners on Application Domains
Francesca Fallucchi | Maria Teresa Pazienza | Fabio Massimo Zanzotto
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In ontology learning from texts, we have ontology-rich domains where we have large structured domain knowledge repositories or we have large general corpora with large general structured knowledge repositories such as WordNet (Miller, 1995). Ontology learning methods are more useful in ontology-poor domains. Yet, in these conditions, these methods have not a particularly high performance as training material is not sufficient. In this paper we present an LSP ontology learning method that can exploit models learned from a generic domain to extract new information in a specific domain. In our model, we firstly learn a model from training data and then we use the learned model to discover knowledge in a specific domain. We tested our model adaptation strategy using a background domain that is applied to learn the isa networks in the Earth Observation Domain as a specific domain. We will demonstrate that our method captures domain knowledge better than other generic models: our model better captures what is expected by domain experts than a baseline method based only on WordNet. This latter is better correlated with non-domain annotators asked to produce the ontology for the specific domain.

2009

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Efficient kernels for sentence pair classification
Fabio Massimo Zanzotto | Lorenzo Dell’Arciprete
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Singular Value Decomposition for Feature Selection in Taxonomy Learning
Francesca Fallucchi | Fabio Massimo Zanzotto
Proceedings of the International Conference RANLP-2009

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SVD Feature Selection for Probabilistic Taxonomy Learning
Francesca Fallucchi | Fabio Massimo Zanzotto
Proceedings of the Workshop on Geometrical Models of Natural Language Semantics

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Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)
Chris Callison-Burch | Ido Dagan | Christopher Manning | Marco Pennacchiotti | Fabio Massimo Zanzotto
Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)

2008

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Yet another Platform for Extracting Knowledge from Corpora
Francesca Fallucchi | Fabio Massimo Zanzotto
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The research field of “extracting knowledge bases from text collections” seems to be mature: its target and its working hypotheses are clear. In this paper we propose a platform, YAPEK, i.e., Yet Another Platform for Extracting Knowledge from corpora, that wants to be the base to collect the majority of algorithms for extracting knowledge bases from corpora. The idea is that, when many knowledge extraction algorithms are collected under the same platform, relative comparisons are clearer and many algorithms can be leveraged to extract more valuable knowledge for final tasks such as Textual Entailment Recognition. As we want to collect many knowledge extraction algorithms, YAPEK is based on the three working hypotheses of the area: the basic hypothesis, the distributional hypothesis, and the point-wise assertion patterns. In YAPEK, these three hypotheses define two spaces: the space of the target textual forms and the space of the contexts. This platform guarantees the possibility of rapidly implementing many models for extracting knowledge from corpora as the platform gives clear entry points to model what is really different in the different algorithms: the feature spaces, the distances in these spaces, and the actual algorithm.

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Encoding Tree Pair-Based Graphs in Learning Algorithms: The Textual Entailment Recognition Case
Alessandro Moschitti | Fabio Massimo Zanzotto
Coling 2008: Proceedings of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing

2007

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Shallow Semantic in Fast Textual Entailment Rule Learners
Fabio Massimo Zanzotto | Marco Pennacchiotti | Alessandro Moschitti
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

2006

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Automatic Learning of Textual Entailments with Cross-Pair Similarities
Fabio Massimo Zanzotto | Alessandro Moschitti
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Discovering Asymmetric Entailment Relations between Verbs Using Selectional Preferences
Fabio Massimo Zanzotto | Marco Pennacchiotti | Maria Teresa Pazienza
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Dependency-based Algorithm for Grammar Conversion
Alessandro Bahgat Shehata | Fabio Massimo Zanzotto
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper we present a model to transfer a grammatical formalism in another. The model is applicable only on restrictive conditions. However, it is fairly useful for many purposes: parsing evaluation, researching methods for truly combining different parsing outputs to reach better parsing performances, and building larger syntactically annotated corpora for data-driven approaches. The model has been tested over a case study: the translation of the Turin Tree Bank Grammar to the Shallow Grammar of the CHAOS Italian parser.

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Mixing WordNet, VerbNet and PropBank for studying verb relations
Maria Teresa Pazienza | Marco Pennacchiotti | Fabio Massimo Zanzotto
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper we present a novel resource for studying the semantics of verb relations. The resource is created by mixing sense relational knowledge enclosed in WordNet, frame knowledge enclosed in VerbNet and corpus knowledge enclosed in PropBank. As a result, a set of about 1000 frame pairs is made available. A frame pair represents a pair of verbs in a peculiar semantic relation accompanied with specific information, such as: the syntactic-semantic frames of the two verbs, the mapping among their thematic roles and a set of textual examples extracted from the PennTreeBank. We specifically focus on four relations: Troponymy, Causation, Entailment and Antonymy. The different steps required for the mapping are described in detail and statistics on resource mutual coverage are reported. We also propose a practical use of the resource for the task of Textual Entailment acquisition and for Question Answering. A first attempt for automate the mapping among verb arguments is also presented: early experiments show that simple techniques can achieve good results, up to 85% F-Measure.

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Similarity between Pairs of Co-indexed Trees for Textual Entailment Recognition
Fabio Massimo Zanzotto | Alessandro Moschitti
Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing

2005

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Discovering Entailment Relations Using “Textual Entailment Patterns”
Fabio Massimo Zanzotto | Maria Teresa Pazienza | Marco Pennacchiotti
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment

2004

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Large Scale Experiments for Semantic Labeling of Noun Phrases in Raw Text
Louise Guthrie | Roberto Basili | Fabio Zanzotto | Kalina Bontcheva | Hamish Cunningham | David Guthrie | Jia Cui | Marco Cammisa | Jerry Cheng-Chieh Liu | Cassia Farria Martin | Kristiyan Haralambiev | Martin Holub | Klaus Macherey | Fredrick Jelinek
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A2Q: An Agent-based Architecure for Multilingual Q&A
Roberto Basili | Nicola Lorusso | Maria Teresa Pazienza | Fabio Massimo Zanzotto
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A Similarity Measure for Unsupervised Semantic Disambiguation
Roberto Basili | Marco Cammisa | Fabio Massimo Zanzotto
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Ontological resources and question answering
Roberto Basili | Dorte H. Hansen | Patrizia Paggio | Maria Teresa Pazienza | Fabio Massimo Zanzotto
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004

2002

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Knowledge-Based Multilingual Document Analysis
R. Basili | R. Catizone | L. Padro | M.T. Pazienza | G. Rigau | A. Setzer | N. Webb | F. Zanzotto
COLING-02: SEMANET: Building and Using Semantic Networks

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Decision Trees as Explicit Domain Term Definitions
Roberto Basili | Maria Teresa Pazienza | Fabio Massimo Zanzotto
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Multilingual Authoring: the NAMIC Approach
Roberto Basili | Maria Teresa Pazienza | Fabio Massimo Zanzotto | Roberta Catizone | Andrea Setzer | Nick Webb | Yorick Wilks | Lluís Padró | German Rigau
Proceedings of the ACL 2001 Workshop on Human Language Technology and Knowledge Management

2000

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Tuning Lexicons to New Operational Scenarios
Roberto Basili | Maria Teresa Pazienza | Michele Vindigni | Fabio Massimo Zanzotto
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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The Italian Syntactic-Semantic Treebank: Architecture, Annotation, Tools and Evaluation
S. Montemagni | F. Barsotti | M. Battista | N. Calzolari | O. Corazzari | A. Zampolli | F. Fanciulli | M. Massetani | R. Raffaelli | R. Basili | M. T. Pazienza | D. Saracino | F. Zanzotto | N. Mana | F. Pianesi | R. Delmonte
Proceedings of the COLING-2000 Workshop on Linguistically Interpreted Corpora