Search Results for author: Simonetta Montemagni

Found 21 papers, 0 papers with code

Making Italian Parliamentary Records Machine-Actionable: the Construction of the ParlaMint-IT corpus

no code implementations ParlaCLARIN (LREC) 2022 Tommaso Agnoloni, Roberto Bartolini, Francesca Frontini, Simonetta Montemagni, Carlo Marchetti, Valeria Quochi, Manuela Ruisi, Giulia Venturi

The corpus contains 1199 sessions and 79, 373 speeches, for a total of about 31 million words and was encoded according to the ParlaCLARIN TEI XML format, as well as in CoNLL-UD format.


Towards the Creation of a Diachronic Corpus for Italian: A Case Study on the GDLI Quotations

no code implementations LT4HALA (LREC) 2022 Manuel Favaro, Elisa Guadagnini, Eva Sassolini, Marco Biffi, Simonetta Montemagni

In this paper we describe some experiments related to a corpus derived from an authoritative historical Italian dictionary, namely the Grande dizionario della lingua italiana (‘Great Dictionary of Italian Language’, in short GDLI).

Lemmatization POS +1

Profiling-UD: a Tool for Linguistic Profiling of Texts

no code implementations LREC 2020 Dominique Brunato, Andrea Cimino, Felice Dell{'}Orletta, Giulia Venturi, Simonetta Montemagni

In this paper, we introduce Profiling{--}UD, a new text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives.

Enhancing Universal Dependency Treebanks: A Case Study

no code implementations WS 2018 Joakim Nivre, Paola Marongiu, Filip Ginter, Jenna Kanerva, Simonetta Montemagni, Sebastian Schuster, Maria Simi

We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies.

Assessing the Impact of Incremental Error Detection and Correction. A Case Study on the Italian Universal Dependency Treebank

no code implementations WS 2018 Chiara Alzetta, Felice Dell{'}Orletta, Simonetta Montemagni, Maria Simi, Giulia Venturi

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.

Dependency Parsing

CItA: an L1 Italian Learners Corpus to Study the Development of Writing Competence

no code implementations LREC 2016 Alessia Barbagli, Pietro Lucisano, Felice Dell{'}Orletta, Simonetta Montemagni, Giulia Venturi

In this paper, we present the CItA corpus (Corpus Italiano di Apprendenti L1), a collection of essays written by Italian L1 learners collected during the first and second year of lower secondary school.

ALT Explored: Integrating an Online Dialectometric Tool and an Online Dialect Atlas

no code implementations LREC 2016 Martijn Wieling, Eva Sassolini, Sebastiana Cucurullo, Simonetta Montemagni

In this paper, we illustrate the integration of an online dialectometric tool, Gabmap, together with an online dialect atlas, the Atlante Lessicale Toscano (ALT-Web).

Less is More? Towards a Reduced Inventory of Categories for Training a Parser for the Italian Stanford Dependencies

no code implementations LREC 2014 Maria Simi, Cristina Bosco, Simonetta Montemagni

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.


T2K\textasciicircum2: a System for Automatically Extracting and Organizing Knowledge from Texts

no code implementations LREC 2014 Felice Dell{'}Orletta, Giulia Venturi, Andrea Cimino, Simonetta Montemagni

In this paper, we present T2K{\textasciicircum}2, a suite of tools for automatically extracting domain―specific knowledge from collections of Italian and English texts.

Enriching the ISST-TANL Corpus with Semantic Frames

no code implementations LREC 2012 Aless Lenci, ro, Simonetta Montemagni, Giulia Venturi, Maria Grazia Cutrull{\`a}

The paper describes the design and the results of a manual annotation methodology devoted to enrich the ISST--TANL Corpus, derived from the Italian Syntactic--Semantic Treebank (ISST), with Semantic Frames information.

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