Search Results for author: Maria Simi

Found 10 papers, 1 papers with code

Dialogue Act and Slot Recognition in Italian Complex Dialogues

no code implementations EURALI (LREC) 2022 Irene Sucameli, Michele De Quattro, Arash Eshghi, Alessandro Suglia, Maria Simi

Since the advent of Transformer-based, pretrained language models (LM) such as BERT, Natural Language Understanding (NLU) components in the form of Dialogue Act Recognition (DAR) and Slot Recognition (SR) for dialogue systems have become both more accurate and easier to create for specific application domains.

Natural Language Understanding

Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies

no code implementations WS 2020 Giuseppe Attardi, Daniele Sartiano, Maria Simi

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.

Dependency Parsing

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

Adapting the TANL tool suite to Universal Dependencies

no code implementations LREC 2016 Maria Simi, Giuseppe Attardi

TANL is a suite of tools for text analytics based on the software architecture paradigm of data driven pipelines.

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.


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