Mihaela Plamada-Onofrei

Also published as: Mihaela Onofrei, Mihaela Plămadă-Onofrei


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.


The Romanian Corpus Annotated with Verbal Multiword Expressions
Verginica Barbu Mititelu | Mihaela Cristescu | Mihaela Onofrei
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

This paper reports on the Romanian journalistic corpus annotated with verbal multiword expressions following the PARSEME guidelines. The corpus is sentence split, tokenized, part-of-speech tagged, lemmatized, syntactically annotated and verbal multiword expressions are identified and classified. It offers insights into the frequency of such Romanian word combinations and allows for their characterization. We offer data about the types of verbal multiword expressions in the corpus and some of their characteristics, such as internal structure, diversity in the corpus, average length, productivity of the verbs. This is a language resource that is important per se, as well as for the task of automatic multiword expressions identification, which can be further used in other systems. It was already used as training and test material in the shared tasks for the automatic identification of verbal multiword expressions organized by PARSEME.


Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies
Mihaela Onofrei | Ionuț Hulub | Diana Trandabăț | Daniela Gîfu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper presents the participation of Apollo’s team in the SemEval-2018 Task 9 “Hypernym Discovery”, Subtask 1: “General-Purpose Hypernym Discovery”, which tries to produce a ranked list of hypernyms for a specific term. We propose a novel approach for automatic extraction of hypernymy relations from a corpus by using dependency patterns. We estimated that the application of these patterns leads to a higher score than using the traditional lexical patterns.


Wild Devs’ at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity
Răzvan-Gabriel Rotari | Ionuț Hulub | Ștefan Oprea | Mihaela Plămadă-Onofrei | Alina Beatrice Lorenţ | Raluca Preisler | Adrian Iftene | Diana Trandabăț
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper presents Wild Devs’ participation in the SemEval-2017 Task 2 “Multi-lingual and Cross-lingual Semantic Word Similarity”, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.