no code implementations • COLING 2020 • Enrica Troiano, Roman Klinger, Sebastian Pad{\'o}
Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world.
no code implementations • ACL 2020 • Erenay Dayanik, Sebastian Pad{\'o}
A central concern in Computational Social Sciences (CSS) is fairness: where the role of NLP is to scale up text analysis to large corpora, the quality of automatic analyses should be as independent as possible of textual properties.
no code implementations • LREC 2020 • Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn, Sebastian Pad{\'o}
DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015.
no code implementations • LREC 2020 • Sean Papay, Sebastian Pad{\'o}
We introduce RiQuA (RIch QUotation Annotations), a corpus that provides quotations, including their interpersonal structure (speakers and addressees) for English literary text.
no code implementations • RANLP 2019 • V Thejas, Abhijeet Gupta, Sebastian Pad{\'o}
Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful {`}background knowledge{'} for numeric prediction, while this is true to a lesser degree in the inverse direction.
no code implementations • RANLP 2019 • Sean Papay, Sebastian Pad{\'o}
The detection of quotations (i. e., reported speech, thought, and writing) has established itself as an NLP analysis task.
1 code implementation • WS 2019 • Moiz Rauf, Sebastian Pad{\'o}
In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods.
1 code implementation • WS 2019 • Josua Stadelmaier, Sebastian Pad{\'o}
A common approach in knowledge base completion (KBC) is to learn representations for entities and relations in order to infer missing facts by generalizing existing ones.
no code implementations • ACL 2019 • Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pad{\'o}
This paper describes the MARDY corpus annotation environment developed for a collaboration between political science and computational linguistics.
1 code implementation • ACL 2019 • Sebastian Pad{\'o}, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn
Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making.
1 code implementation • WS 2019 • Martin Riedl, Daniela Betz, Sebastian Pad{\'o}
This article focuses on the problem of identifying articles and recovering their text from within and across newspaper pages when OCR just delivers one text file per page.
no code implementations • WS 2019 • Jennifer Sikos, Sebastian Pad{\'o}
This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories.
no code implementations • EMNLP 2018 • Heike Adel, Laura Ana Maria Bostan, Sean Papay, Sebastian Pad{\'o}, Roman Klinger
As a result, comparability of models across tasks is missing and their applicability to new tasks is limited.
no code implementations • COLING 2018 • Jennifer Sikos, Sebastian Pad{\'o}
Much interest in Frame Semantics is fueled by the substantial extent of its applicability across languages.
no code implementations • ACL 2018 • Martin Riedl, Sebastian Pad{\'o}
We ask how to practically build a model for German named entity recognition (NER) that performs at the state of the art for both contemporary and historical texts, i. e., a big-data and a small-data scenario.
no code implementations • NAACL 2018 • Maja Buljan, Sebastian Pad{\'o}, Jan {\v{S}}najder
LexSub is a more natural task, enables us to evaluate meaning composition at the level of individual words, and provides a common ground to compare CDSMs with dedicated LexSub models.
no code implementations • WS 2018 • Sean Papay, Sebastian Pad{\'o}, Ngoc Thang Vu
Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words.
no code implementations • WS 2017 • Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Pad{\'o}, Roman Klinger
There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification).
no code implementations • WS 2017 • Evgeny Kim, Sebastian Pad{\'o}, Roman Klinger
Literary genres are commonly viewed as being defined in terms of content and stylistic features.
no code implementations • SEMEVAL 2017 • Zoran Medi{\'c}, Jan {\v{S}}najder, Sebastian Pad{\'o}
The Practical Lexical Function (PLF) model is a model of computational distributional semantics that attempts to strike a balance between expressivity and learnability in predicting phrase meaning and shows competitive results.
no code implementations • SEMEVAL 2017 • Abhijeet Gupta, Gemma Boleda, Sebastian Pad{\'o}
Word embeddings are supposed to provide easy access to semantic relations such as {``}male of{''} (man{--}woman).
no code implementations • EACL 2017 • Gemma Boleda, Abhijeet Gupta, Sebastian Pad{\'o}
Instances ({``}Mozart{''}) are ontologically distinct from concepts or classes ({``}composer{''}).
no code implementations • COLING 2016 • Sebastian Pad{\'o}, Aur{\'e}lie Herbelot, Max Kisselew, Jan {\v{S}}najder
Compositional distributional semantic models (CDSMs) have successfully been applied to the task of predicting the meaning of a range of linguistic constructions.
no code implementations • LREC 2014 • Francesca Frontini, Valeria Quochi, Sebastian Pad{\'o}, Monica Monachini, Jason Utt
An experiment is presented to induce a set of polysemous basic type alternations (such as Animal-Food, or Building-Institution) by deriving them from the sense alternations found in an existing lexical resource.
no code implementations • TACL 2014 • Jason Utt, Sebastian Pad{\'o}
Syntax-based distributional models of lexical semantics provide a flexible and linguistically adequate representation of co-occurrence information.
no code implementations • LREC 2012 • Amalia Todirascu, Sebastian Pad{\'o}, Jennifer Krisch, Max Kisselew, Ulrich Heid
This paper presents some of the results of the CLASSYN project which investigated the classification of text according to audience-related text types.