no code implementations • 20 Mar 2024 • Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer Van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive.
1 code implementation • ACL 2022 • David Samuel, Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text.
2 code implementations • ACL 2021 • Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal
Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e, g,, target extraction or targeted polarity classification.
2 code implementations • NoDaLiDa 2021 • Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen
We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training.
1 code implementation • CONLL 2020 • Lasha Abzianidze, Johan Bos, Stephan Oepen
Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse.
no code implementations • CONLL 2020 • Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajic, Daniel Hershcovich, Bin Li, Tim O{'}Gorman, Nianwen Xue, Daniel Zeman
Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.
no code implementations • WS 2020 • Robin Kurtz, Stephan Oepen, Marco Kuhlmann
We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem.
no code implementations • LREC 2020 • Maja Buljan, Joakim Nivre, Stephan Oepen, Lilja {\O}vrelid
We discuss methodological choices in contrastive and diagnostic evaluation in meaning representation parsing, i. e. mapping from natural language utterances to graph-based encodings of its semantic structure.
no code implementations • CONLL 2019 • Stephan Oepen, Dan Flickinger
The English Resource Grammar (ERG) is a broad-coverage computational grammar of English that outputs underspecified logical-form representations of meaning in a framework dubbed English Resource Semantics (ERS).
no code implementations • CONLL 2019 • Stephan Oepen, Omri Abend, Jan Hajic, Daniel Hershcovich, Marco Kuhlmann, Tim O{'}Gorman, Nianwen Xue, Jayeol Chun, Milan Straka, Zdenka Uresova
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks.
1 code implementation • ACL 2019 • Alex Koller, er, Stephan Oepen, Weiwei Sun
This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs.
no code implementations • EMNLP 2018 • Murhaf Fares, Stephan Oepen, Erik Velldal
In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun{--}noun compounds.
no code implementations • CONLL 2018 • Murhaf Fares, Stephan Oepen, Lilja {\O}vrelid, Jari Bj{\"o}rne, Richard Johansson
We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018).
1 code implementation • 18 Sep 2018 • Murhaf Fares, Stephan Oepen, Erik Velldal
In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun--noun compounds.
no code implementations • WS 2017 • Richard Eckart de Castilho, Nancy Ide, Emanuele Lapponi, Stephan Oepen, Keith Suderman, Erik Velldal, Marc Verhagen
We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.
no code implementations • LREC 2016 • Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinkov{\'a}, Dan Flickinger, Jan Haji{\v{c}}, Angelina Ivanova, Zde{\v{n}}ka Ure{\v{s}}ov{\'a}
We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions.
no code implementations • LREC 2014 • Emanuele Lapponi, Erik Velldal, Stephan Oepen, Rune Lain Knudsen
The Linguistic Annotation Framework (LAF) provides an abstract data model for specifying interchange representations to ensure interoperability among different annotation formats.
no code implementations • LREC 2014 • Milen Kouylekov, Stephan Oepen
With growing interest in the creation and search of linguistic annotations that form general graphs (in contrast to formally simpler, rooted trees), there also is an increased need for infrastructures that support the exploration of such representations, for example logical-form meaning representations or semantic dependency graphs.
no code implementations • LREC 2014 • Dan Flickinger, Emily M. Bender, Stephan Oepen
We motivate and describe the design and development of an emerging encyclopedia of compositional semantics, pursuing three objectives.
no code implementations • LREC 2012 • Jonathon Read, Dan Flickinger, Rebecca Dridan, Stephan Oepen, Lilja {\O}vrelid
We present the WeSearch Data Collection (WDC)―a freely redistributable, partly annotated, comprehensive sample of User-Generated Content.