Andrei Scutelnicu


2020

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CoBiLiRo: A Research Platform for Bimodal Corpora
Dan Cristea | Ionuț Pistol | Șerban Boghiu | Anca-Diana Bibiri | Daniela Gîfu | Andrei Scutelnicu | Mihaela Onofrei | Diana Trandabăț | George Bugeag
Proceedings of the 1st International Workshop on Language Technology Platforms

This paper describes the on-going work carried out within the CoBiLiRo (Bimodal Corpus for Romanian Language) research project, part of ReTeRom (Resources and Technologies for Developing Human-Machine Interfaces in Romanian). Data annotation finds increasing use in speech recognition and synthesis with the goal to support learning processes. In this context, a variety of different annotation systems for application to Speech and Text Processing environments have been presented. Even if many designs for the data annotations workflow have emerged, the process of handling metadata, to manage complex user-defined annotations, is not covered enough. We propose a design of the format aimed to serve as an annotation standard for bimodal resources, which facilitates searching, editing and statistical analysis operations over it. The design and implementation of an infrastructure that houses the resources are also presented. The goal is widening the dissemination of bimodal corpora for research valorisation and use in applications. Also, this study reports on the main operations of the web Platform which hosts the corpus and the automatic conversion flows that brings the submitted files at the format accepted by the Platform.

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Adding a Syntactic Annotation Level to the Corpus of Contemporary Romanian Language
Andrei Scutelnicu | Catalina Maranduc | Dan Cristea
Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora

In this paper we present an experiment of augmenting the Corpus of Contemporary Romanian Language (CoRoLa) with the syntactic level of annotations, which would allow users to address queries about the syntax of Romanian sentences, in the Universal Dependency model. After a short introduction of CoRoLa, we describe the treebanks used to train the dependency parser, we show the evaluation results and the process of upgrading CoRoLa with the new level of annotations. The parser displaying the best accuracy with respect to recognition of heads and relations, out of three variants trained on manually built treebanks, was chosen. Keywords: Syntactic annotation, treebank, corpus, maltparser