Search Results for author: Stefan Ruseti

Found 8 papers, 1 papers with code

Interpretable Identification of Cybersecurity Vulnerabilities from News Articles

no code implementations RANLP 2021 Pierre Frode de la Foret, Stefan Ruseti, Cristian Sandescu, Mihai Dascalu, Sebastien Travadel

Various automated processing pipelines grounded in Natural Language Processing techniques for text classification were introduced for the early identification of vulnerabilities starting from Open-Source Intelligence (OSINT) data, including news websites, blogs, and social media.

text-classification Text Classification

RoBERT -- A Romanian BERT Model

no code implementations COLING 2020 Mihai Masala, Stefan Ruseti, Mihai Dascalu

Deep pre-trained language models tend to become ubiquitous in the field of Natural Language Processing (NLP).

Sentiment Analysis Transfer Learning

Romanian Diacritics Restoration Using Recurrent Neural Networks

no code implementations6 Sep 2020 Stefan Ruseti, Teodor-Mihai Cotet, Mihai Dascalu

Diacritics restoration is a mandatory step for adequately processing Romanian texts, and not a trivial one, as you generally need context in order to properly restore a character.

Answering questions by learning to rank - Learning to rank by answering questions

no code implementations IJCNLP 2019 George Sebastian Pirtoaca, Traian Rebedea, Stefan Ruseti

Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing.

Learning-To-Rank Multiple-choice

Answering questions by learning to rank -- Learning to rank by answering questions

no code implementations2 Sep 2019 George-Sebastian Pîrtoacă, Traian Rebedea, Stefan Ruseti

Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing.

Learning-To-Rank Multiple-choice

Improving Retrieval-Based Question Answering with Deep Inference Models

1 code implementation7 Dec 2018 George-Sebastian Pirtoaca, Traian Rebedea, Stefan Ruseti

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer.

Information Retrieval Natural Language Inference +2

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