Maria Simi


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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.


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Assessing the Impact of Incremental Error Detection and Correction. A Case Study on the Italian Universal Dependency Treebank
Chiara Alzetta | Felice Dell’Orletta | Simonetta Montemagni | Maria Simi | Giulia Venturi
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

Detection and correction of errors and inconsistencies in “gold treebanks” are becoming more and more central topics of corpus annotation. The paper illustrates a new incremental method for enhancing treebanks, with particular emphasis on the extension of error patterns across different textual genres and registers. Impact and role of corrections have been assessed in a dependency parsing experiment carried out with four different parsers, whose results are promising. For both evaluation datasets, the performance of parsers increases, in terms of the standard LAS and UAS measures and of a more focused measure taking into account only relations involved in error patterns, and at the level of individual dependencies.

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Enhancing Universal Dependency Treebanks: A Case Study
Joakim Nivre | Paola Marongiu | Filip Ginter | Jenna Kanerva | Simonetta Montemagni | Sebastian Schuster | Maria Simi
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies. We apply a rule-based system developed for English and a data-driven system trained on Finnish to Swedish and Italian. We find that both systems are accurate enough to bootstrap enhanced dependencies in existing UD treebanks. In the case of Italian, results are even on par with those of a prototype language-specific system.


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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Martin Popel | Milan Straka | Jan Hajič | Joakim Nivre | Filip Ginter | Juhani Luotolahti | Sampo Pyysalo | Slav Petrov | Martin Potthast | Francis Tyers | Elena Badmaeva | Memduh Gokirmak | Anna Nedoluzhko | Silvie Cinková | Jan Hajič jr. | Jaroslava Hlaváčová | Václava Kettnerová | Zdeňka Urešová | Jenna Kanerva | Stina Ojala | Anna Missilä | Christopher D. Manning | Sebastian Schuster | Siva Reddy | Dima Taji | Nizar Habash | Herman Leung | Marie-Catherine de Marneffe | Manuela Sanguinetti | Maria Simi | Hiroshi Kanayama | Valeria de Paiva | Kira Droganova | Héctor Martínez Alonso | Çağrı Çöltekin | Umut Sulubacak | Hans Uszkoreit | Vivien Macketanz | Aljoscha Burchardt | Kim Harris | Katrin Marheinecke | Georg Rehm | Tolga Kayadelen | Mohammed Attia | Ali Elkahky | Zhuoran Yu | Emily Pitler | Saran Lertpradit | Michael Mandl | Jesse Kirchner | Hector Fernandez Alcalde | Jana Strnadová | Esha Banerjee | Ruli Manurung | Antonio Stella | Atsuko Shimada | Sookyoung Kwak | Gustavo Mendonça | Tatiana Lando | Rattima Nitisaroj | Josie Li
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.


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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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Less is More? Towards a Reduced Inventory of Categories for Training a Parser for the Italian Stanford Dependencies
Maria Simi | Cristina Bosco | Simonetta Montemagni
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Stanford Dependencies (SD) represent nowadays a de facto standard as far as dependency annotation is concerned. The goal of this paper is to explore pros and cons of different strategies for generating SD annotated Italian texts to enrich the existing Italian Stanford Dependency Treebank (ISDT). This is done by comparing the performance of a statistical parser (DeSR) trained on a simpler resource (the augmented version of the Merged Italian Dependency Treebank or MIDT+) and whose output was automatically converted to SD, with the results of the parser directly trained on ISDT. Experiments carried out to test reliability and effectiveness of the two strategies show that the performance of a parser trained on the reduced dependencies repertoire, whose output can be easily converted to SD, is slightly higher than the performance of a parser directly trained on ISDT. A non-negligible advantage of the first strategy for generating SD annotated texts is that semi-automatic extensions of the training resource are more easily and consistently carried out with respect to a reduced dependency tag set. Preliminary experiments carried out for generating the collapsed and propagated SD representation are also reported.


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Converting Italian Treebanks: Towards an Italian Stanford Dependency Treebank
Cristina Bosco | Simonetta Montemagni | Maria Simi
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse


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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.


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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.


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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)