Search Results for author: Stephan Oepen

Found 43 papers, 6 papers with code

A New Massive Multilingual Dataset for High-Performance Language Technologies

no code implementations20 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.

Language Modelling Machine Translation +2

Direct parsing to sentiment graphs

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.

Sentiment Analysis

Structured Sentiment Analysis as Dependency Graph Parsing

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.

Sentiment Analysis

Large-Scale Contextualised Language Modelling for Norwegian

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.

Language Modelling

DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs

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.

MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

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.

Sentence

End-to-End Negation Resolution as Graph Parsing

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.

Negation

A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing

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.

Dependency Parsing

The ERG at MRP 2019: Radically Compositional Semantic Dependencies

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).

MRP 2019: Cross-Framework Meaning Representation Parsing

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.

Sentence

Graph-Based Meaning Representations: Design and Processing

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.

Sentence

Transfer and Multi-Task Learning for Noun--Noun Compound Interpretation

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.

General Classification Information Retrieval +3

Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation

1 code implementation18 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.

Classification General Classification +1

Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs

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.

Semantic Technologies for Querying Linguistic Annotations: An Experiment Focusing on Graph-Structured Data

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.

Sentence

Off-Road LAF: Encoding and Processing Annotations in NLP Workflows

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.

Part-Of-Speech Tagging

The WeSearch Corpus, Treebank, and Treecache -- A Comprehensive Sample of User-Generated Content

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.

Domain Adaptation

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