no code implementations • EACL 2021 • Deborah Ferreira, Andr{\'e} Freitas
Mathematical statements written in natural language are usually composed of two different modalities: mathematical elements and natural language.
no code implementations • ACL 2020 • Deborah Ferreira, Andr{\'e} Freitas
The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text.
no code implementations • WS 2019 • Viktor Schlegel, Andr{\'e} Freitas
This paper describes DBee, a database to support the construction of data-intensive AI applications.
no code implementations • WS 2019 • Christina Niklaus, Matthias Cetto, Andr{\'e} Freitas, H, Siegfried schuh
We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications.
no code implementations • SEMEVAL 2017 • Juliano Sales, H, Siegfried schuh, Andr{\'e} Freitas
This task proposes a challenge to support the interaction between users and applications, micro-services and software APIs using natural language.
no code implementations • SEMEVAL 2017 • Keith Cortis, Andr{\'e} Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, H, Siegfried schuh, Brian Davis
This paper discusses the {``}Fine-Grained Sentiment Analysis on Financial Microblogs and News{''} task as part of SemEval-2017, specifically under the {``}Detecting sentiment, humour, and truth{''} theme.
no code implementations • WS 2016 • Vivian Silva, H, Siegfried schuh, Andr{\'e} Freitas
Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.
no code implementations • LREC 2016 • Frederico Tommasi Caroli, Andr{\'e} Freitas, Jo{\~a}o Carlos Pereira da Silva, H, Siegfried schuh
Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications.