Giuseppe Attardi


2021

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Biaffine Dependency and Semantic Graph Parsing for EnhancedUniversal Dependencies
Giuseppe Attardi | Daniele Sartiano | Maria Simi
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

This paper presents the system used in our submission to the IWPT 2021 Shared Task. This year the official evaluation metrics was ELAS, therefore dependency parsing might have been avoided as well as other pipeline stages like POS tagging and lemmatization. We nevertheless chose to deploy a combination of a dependency parser and a graph parser. The dependency parser is a biaffine parser, that uses transformers for representing input sentences, with no other feature. The graph parser is a semantic parser that exploits a similar architecture except for using a sigmoid crossentropy loss function to return multiple values for the predicted arcs. The final output is obtained by merging the output of the two parsers. The dependency parser achieves top or close to top LAS performance with respect to other systems that report results on such metrics, except on low resource languages (Tamil, Estonian, Latvian).

2020

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Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task
Valeriya Slovikovskaya | Giuseppe Attardi
Proceedings of the 12th Language Resources and Evaluation Conference

Transformer models, trained and publicly released over the last couple of years, have proved effective in many NLP tasks. We wished to test their usefulness in particular on the stance detection task. We performed experiments on the data from the Fake News Challenge Stage 1 (FNC-1). We were indeed able to improve the reported SotA on the challenge, by exploiting the generalization power of large language models based on Transformer architecture. Specifically (1) we improved the FNC-1 best performing model adding BERT sentence embedding of input sequences as a model feature, (2) we fine-tuned BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained state-of-the-art results on FNC-1 task.

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Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies
Giuseppe Attardi | Daniele Sartiano | Maria Simi
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm. We train separate models for each language on the shared task data. We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission. For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.

2017

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FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
Giuseppe Attardi | Antonio Carta | Federico Errica | Andrea Madotto | Ludovica Pannitto
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results.

2016

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Adapting the TANL tool suite to Universal Dependencies
Maria Simi | Giuseppe Attardi
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

TANL is a suite of tools for text analytics based on the software architecture paradigm of data driven pipelines. The strategies for upgrading TANL to the use of Universal Dependencies range from a minimalistic approach consisting of introducing pre/post-processing steps into the native pipeline to revising the whole pipeline. We explore the issue in the context of the Italian Treebank, considering both the efforts involved, how to avoid losing linguistically relevant information and the loss of accuracy in the process. In particular we compare different strategies for parsing and discuss the implications of simplifying the pipeline when detailed part-of-speech and morphological annotations are not available, as it is the case for less resourceful languages. The experiments are relative to the Italian linguistic pipeline, but the use of different parsers in our evaluations and the avoidance of language specific tagging make the results general enough to be useful in helping the transition to UD for other languages.

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UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi | Daniele Sartiano
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation
Antonio Valerio Miceli-Barone | Giuseppe Attardi
Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation

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DeepNL: a Deep Learning NLP pipeline
Giuseppe Attardi
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation
Antonio Valerio Miceli-Barone | Giuseppe Attardi
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)

2014

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UniPi: Recognition of Mentions of Disorders in Clinical Text
Giuseppe Attardi | Vittoria Cozza | Daniele Sartiano
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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Pre-Reordering for Machine Translation Using Transition-Based Walks on Dependency Parse Trees
Antonio Valerio Miceli-Barone | Giuseppe Attardi
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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Dependency Parsing Domain Adaptation using Transductive SVM
Antonio Valerio Miceli-Barone | Giuseppe Attardi
Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP

2011

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A Dependency Based Statistical Translation Model
Giuseppe Attardi | Atanas Chanev | Antonio Valerio Miceli Barone
Proceedings of Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation

2010

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TANL-1: Coreference Resolution by Parse Analysis and Similarity Clustering
Giuseppe Attardi | Maria Simi | Stefano Dei Rossi
Proceedings of the 5th International Workshop on Semantic Evaluation

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Comparing the Influence of Different Treebank Annotations on Dependency Parsing
Cristina Bosco | Simonetta Montemagni | Alessandro Mazzei | Vincenzo Lombardo | Felice Dell’Orletta | Alessandro Lenci | Leonardo Lesmo | Giuseppe Attardi | Maria Simi | Alberto Lavelli | Johan Hall | Jens Nilsson | Joakim Nivre
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

As the interest of the NLP community grows to develop several treebanks also for languages other than English, we observe efforts towards evaluating the impact of different annotation strategies used to represent particular languages or with reference to particular tasks. This paper contributes to the debate on the influence of resources used for the training and development on the performance of parsing systems. It presents a comparative analysis of the results achieved by three different dependency parsers developed and tested with respect to two treebanks for the Italian language, namely TUT and ISST--TANL, which differ significantly at the level of both corpus composition and adopted dependency representations.

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A Resource and Tool for Super-sense Tagging of Italian Texts
Giuseppe Attardi | Stefano Dei Rossi | Giulia Di Pietro | Alessandro Lenci | Simonetta Montemagni | Maria Simi
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

A SuperSense Tagger is a tool for the automatic analysis of texts that associates to each noun, verb, adjective and adverb a semantic category within a general taxonomy. The developed tagger, based on a statistical model (Maximum Entropy), required the creation of an Italian annotated corpus, to be used as a training set, and the improvement of various existing tools. The obtained results significantly improved the current state-of-the art for this particular task.

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Active Learning for Building a Corpus of Questions for Parsing
Jordi Atserias | Giuseppe Attardi | Maria Simi | Hugo Zaragoza
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes how we built a dependency Treebank for questions. The questions for the Treebank were drawn from questions from the TREC 10 QA task and from Yahoo! Answers. Among the uses for the corpus is to train a dependency parser achieving good accuracy on parsing questions without hurting its overall accuracy. We also explore active learning techniques to determine the suitable size for a corpus of questions in order to achieve adequate accuracy while minimizing the annotation efforts.

2009

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Reverse Revision and Linear Tree Combination for Dependency Parsing
Giuseppe Attardi | Felice Dell’Orletta
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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DeSRL: A Linear-Time Semantic Role Labeling System
Massimiliano Ciaramita | Giuseppe Attardi | Felice Dell’Orletta | Mihai Surdeanu
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

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Semantically Annotated Snapshot of the English Wikipedia
Jordi Atserias | Hugo Zaragoza | Massimiliano Ciaramita | Giuseppe Attardi
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes SW1, the first version of a semantically annotated snapshot of the English Wikipedia. In recent years Wikipedia has become a valuable resource for both the Natural Language Processing (NLP) community and the Information Retrieval (IR) community. Although NLP technology for processing Wikipedia already exists, not all researchers and developers have the computational resources to process such a volume of information. Moreover, the use of different versions of Wikipedia processed differently might make it difficult to compare results. The aim of this work is to provide easy access to syntactic and semantic annotations for researchers of both NLP and IR communities by building a reference corpus to homogenize experiments and make results comparable. These resources, a semantically annotated corpus and a “entity containment” derived graph, are licensed under the GNU Free Documentation License and available from http://www.yr-bcn.es/semanticWikipedia

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Comparing Italian parsers on a common Treebank: the EVALITA experience
Cristina Bosco | Alessandro Mazzei | Vincenzo Lombardo | Giuseppe Attardi | Anna Corazza | Alberto Lavelli | Leonardo Lesmo | Giorgio Satta | Maria Simi
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The EVALITA 2007 Parsing Task has been the first contest among parsing systems for Italian. It is the first attempt to compare the approaches and the results of the existing parsing systems specific for this language using a common treebank annotated using both a dependency and a constituency-based format. The development data set for this parsing competition was taken from the Turin University Treebank, which is annotated both in dependency and constituency format. The evaluation metrics were those standardly applied in CoNLL and PARSEVAL. The results of the parsing results are very promising and higher than the state-of-the-art for dependency parsing of Italian. An analysis of such results is provided, which takes into account other experiences in treebank-driven parsing for Italian and for other Romance languages (in particular, the CoNLL X & 2007 shared tasks for dependency parsing). It focuses on the characteristics of data sets, i.e. type of annotation and size, parsing paradigms and approaches applied also to languages other than Italian.

2007

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Tree Revision Learning for Dependency Parsing
Giuseppe Attardi | Massimiliano Ciaramita
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Dependency Parsing with Second-Order Feature Maps and Annotated Semantic Information
Massimiliano Ciaramita | Giuseppe Attardi
Proceedings of the Tenth International Conference on Parsing Technologies

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Multilingual Dependency Parsing and Domain Adaptation using DeSR
Giuseppe Attardi | Felice Dell’Orletta | Maria Simi | Atanas Chanev | Massimiliano Ciaramita
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Experiments with a Multilanguage Non-Projective Dependency Parser
Giuseppe Attardi
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)