Georg Rehm


2022

pdf bib
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
Qian Ruan | Malte Ostendorff | Georg Rehm
Findings of the Association for Computational Linguistics: ACL 2022

Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model based on a pre-trained, encoder-only Transformer language model (HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on PubMed and arXiv substantially. Using various experimental settings on three datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected. It is also observed that the more conspicuous hierarchical structure the dataset has, the larger improvements our method gains. The ablation study demonstrates that the hierarchical position information is the main contributor to our model’s SOTA performance.

pdf bib
Claim Extraction and Law Matching for COVID-19-related Legislation
Niklas Dehio | Malte Ostendorff | Georg Rehm
Proceedings of the Thirteenth Language Resources and Evaluation Conference

To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws. We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. We examine the feasibility of the two tasks concerning claims about COVID-19-related laws from Berlin, Germany. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve 46.7 F1 for claim extraction and 91.4 F1 for law matching, albeit with some conceptual limitations. Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks.

pdf bib
Making a Semantic Event-type Ontology Multilingual
Zdenka Uresova | Karolina Zaczynska | Peter Bourgonje | Eva Fučíková | Georg Rehm | Jan Hajic
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present an extension of the SynSemClass Event-type Ontology, originally conceived as a bilingual Czech-English resource. We added German entries to the classes representing the concepts of the ontology. Having a different starting point than the original work (unannotated parallel corpus without links to a valency lexicon and, of course, different existing lexical resources), it was a challenge to adapt the annotation guidelines, the data model and the tools used for the original version. We describe the process and results of working in such a setup. We also show the next steps to adapt the annotation process, data structures and formats and tools necessary to make the addition of a new language in the future more smooth and efficient, and possibly to allow for various teams to work on SynSemClass extensions to many languages concurrently. We also present the latest release which contains the results of adding German, freely available for download as well as for online access.

pdf bib
Generating Extended and Multilingual Summaries with Pre-trained Transformers
Rémi Calizzano | Malte Ostendorff | Qian Ruan | Georg Rehm
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended summaries of approx. 11 sentences. The dataset comprises 39,626 summaries which are news articles from Wikinews and their sources. We compare three multilingual transformer models on the extractive summarisation task and three training scenarios on which we fine-tune mT5 to perform abstractive summarisation. This results in strong baselines for both extractive and abstractive summarisation on WikinewsSum. We also show how the combination of an extractive model with an abstractive one can be used to create extended abstractive summaries from long input documents. Finally, our results show that fine-tuning mT5 on all the languages combined significantly improves the summarisation performance on low-resource languages.

pdf bib
A Dataset of Offensive German Language Tweets Annotated for Speech Acts
Melina Plakidis | Georg Rehm
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present a dataset consisting of German offensive and non-offensive tweets, annotated for speech acts. These 600 tweets are a subset of the dataset by Struß et al. (2019) and comprises three levels of annotation, i.e., six coarse-grained speech acts, 23 fine-grained speech acts and 14 different sentence types. Furthermore, we provide an evaluation in both qualitative and quantitative terms. The dataset is made publicly available under a CC-BY-4.0 license.

pdf bib
Semantic Relations between Text Segments for Semantic Storytelling: Annotation Tool - Dataset - Evaluation
Michael Raring | Malte Ostendorff | Georg Rehm
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Semantic Storytelling describes the goal to automatically and semi-automatically generate stories based on extracted, processed, classified and annotated information from large content resources. Essential is the automated processing of text segments extracted from different content resources by identifying the relevance of a text segment to a topic and its semantic relation to other text segments. In this paper we present an approach to create an automatic classifier for semantic relations between extracted text segments from different news articles. We devise custom annotation guidelines based on various discourse structure theories and annotate a dataset of 2,501 sentence pairs extracted from 2,638 Wikinews articles. For the annotation, we developed a dedicated annotation tool. Based on the constructed dataset, we perform initial experiments with Transformer language models that are trained for the automatic classification of semantic relations. Our results with promising high accuracy scores suggest the validity and applicability of our approach for future Semantic Storytelling solutions.

pdf bib
Towards Practical Semantic Interoperability in NLP Platforms
Julian Moreno-Schneider | Rémi Calizzano | Florian Kintzel | Georg Rehm | Dimitris Galanis | Ian Roberts
Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022

Interoperability is a necessity for the resolution of complex tasks that require the interconnection of several NLP services. This article presents the approaches that were adopted in three scenarios to address the respective interoperability issues. The first scenario describes the creation of a common REST API for a specific platform, the second scenario presents the interconnection of several platforms via mapping of different representation formats and the third scenario shows the complexities of interoperability through semantic schema mapping or automatic translation.

pdf bib
Overview of the ELE Project
Itziar Aldabe | Jane Dunne | Aritz Farwell | Owen Gallagher | Federico Gaspari | Maria Giagkou | Jan Hajic | Jens Peter Kückens | Teresa Lynn | Georg Rehm | German Rigau | Katrin Marheinecke | Stelios Piperidis | Natalia Resende | Tea Vojtěchová | Andy Way
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

This paper provides an overview of the ongoing European Language Equality(ELE) project, an 18-month action funded by the European Commission which involves 52 partners. The primary goal of ELE is to prepare the European Language Equality Programme, in the form of a strategic research, innovation and implementation agenda and a roadmap for achieving full digital language equality (DLE) in Europe by 2030.

pdf bib
Introducing the Digital Language Equality Metric: Technological Factors
Federico Gaspari | Owen Gallagher | Georg Rehm | Maria Giagkou | Stelios Piperidis | Jane Dunne | Andy Way
Proceedings of the Workshop Towards Digital Language Equality within the 13th Language Resources and Evaluation Conference

This paper introduces the concept of Digital Language Equality (DLE) developed by the EU-funded European Language Equality (ELE) project, and describes the associated DLE Metric with a focus on its technological factors (TFs), which are complemented by situational contextual factors. This work aims at objectively describing the level of technological support of all European languages and lays the foundation to implement a large-scale EU-wide programme to ensure that these languages can continue to exist and prosper in the digital age, to serve the present and future needs of their speakers. The paper situates this ongoing work with a strong European focus in the broader context of related efforts, and explains how the DLE Metric can help track the progress towards DLE for all languages of Europe, focusing in particular on the role played by the TFs. These are derived from the European Language Grid (ELG) Catalogue, that provides the empirical basis to measure the level of digital readiness of all European languages. The DLE Metric scores can be consulted through an online interactive dashboard to show the level of technological support of each European language and track the overall progress toward DLE.

pdf bib
Introducing the Digital Language Equality Metric: Contextual Factors
Annika Grützner-Zahn | Georg Rehm
Proceedings of the Workshop Towards Digital Language Equality within the 13th Language Resources and Evaluation Conference

In our digital age, digital language equality is an important goal to enable participation in society for all citizens, independent of the language they speak. To assess the current state of play with regard to Europe’s languages, we developed, in the project European Language Equality, a metric for digital language equality that consists of two parts, technological and contextual (i.e., non-technological) factors. We present a metric for calculating the contextual factors for over 80 European languages. For each language, a score is calculated that reflects the broader context or socio-economic ecosystem of a language, which has, for a given language, a direct impact for technology and resource development; it is important to note, though, that Language Technologies and Resources related aspects are reflected by the technological factors. To reduce the vast number of potential contextual factors to an adequate number, five different configurations were calculated and evaluated with a panel of experts. The best results were achieved by a configuration in which 12 manually curated factors were included. In the factor selection process, attention was paid to data quality, automatic updatability, inclusion of data from different domains, and a balance between different data types. The evaluation shows that this specific configuration is stable for the official EU languages; while for regional and minority languages, as well as national non-official EU languages, there is room for improvement.

pdf bib
Modelling Cultural and Socio-Economic Dimensions of Political Bias in German Tweets
Aishwarya Anegundi | Konstantin Schulz | Christian Rauh | Georg Rehm
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)

2021

pdf bib
Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments
Dmitrii Aksenov | Peter Bourgonje | Karolina Zaczynska | Malte Ostendorff | Julian Moreno-Schneider | Georg Rehm
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

We present a data set consisting of German news articles labeled for political bias on a five-point scale in a semi-supervised way. While earlier work on hyperpartisan news detection uses binary classification (i.e., hyperpartisan or not) and English data, we argue for a more fine-grained classification, covering the full political spectrum (i.e., far-left, left, centre, right, far-right) and for extending research to German data. Understanding political bias helps in accurately detecting hate speech and online abuse. We experiment with different classification methods for political bias detection. Their comparatively low performance (a macro-F1 of 43 for our best setup, compared to a macro-F1 of 79 for the binary classification task) underlines the need for more (balanced) data annotated in a fine-grained way.

pdf bib
Extraction and Normalization of Vague Time Expressions in German
Ulrike May | Karolina Zaczynska | Julián Moreno-Schneider | Georg Rehm
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

pdf bib
European Language Grid: A Joint Platform for the European Language Technology Community
Georg Rehm | Stelios Piperidis | Kalina Bontcheva | Jan Hajic | Victoria Arranz | Andrejs Vasiļjevs | Gerhard Backfried | Jose Manuel Gomez-Perez | Ulrich Germann | Rémi Calizzano | Nils Feldhus | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Julian Moreno-Schneider | Dimitris Galanis | Penny Labropoulou | Miltos Deligiannis | Katerina Gkirtzou | Athanasia Kolovou | Dimitris Gkoumas | Leon Voukoutis | Ian Roberts | Jana Hamrlova | Dusan Varis | Lukas Kacena | Khalid Choukri | Valérie Mapelli | Mickaël Rigault | Julija Melnika | Miro Janosik | Katja Prinz | Andres Garcia-Silva | Cristian Berrio | Ondrej Klejch | Steve Renals
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.

pdf bib
DFKI SLT at GermEval 2021: Multilingual Pre-training and Data Augmentation for the Classification of Toxicity in Social Media Comments
Remi Calizzano | Malte Ostendorff | Georg Rehm
Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments

We present our submission to the first subtask of GermEval 2021 (classification of German Facebook comments as toxic or not). Binary sequence classification is a standard NLP task with known state-of-the-art methods. Therefore, we focus on data preparation by using two different techniques: task-specific pre-training and data augmentation. First, we pre-train multilingual transformers (XLM-RoBERTa and MT5) on 12 hatespeech detection datasets in nine different languages. In terms of F1, we notice an improvement of 10% on average, using task-specific pre-training. Second, we perform data augmentation by labelling unlabelled comments, taken from Facebook, to increase the size of the training dataset by 79%. Models trained on the augmented training dataset obtain on average +0.0282 (+5%) F1 score compared to models trained on the original training dataset. Finally, the combination of the two techniques allows us to obtain an F1 score of 0.6899 with XLM- RoBERTa and 0.6859 with MT5. The code of the project is available at: https://github.com/airKlizz/germeval2021toxic.

2020

pdf bib
Proceedings of the 1st International Workshop on Language Technology Platforms
Georg Rehm | Kalina Bontcheva | Khalid Choukri | Jan Hajič | Stelios Piperidis | Andrejs Vasiļjevs
Proceedings of the 1st International Workshop on Language Technology Platforms

pdf bib
A Workflow Manager for Complex NLP and Content Curation Workflows
Julian Moreno-Schneider | Peter Bourgonje | Florian Kintzel | Georg Rehm
Proceedings of the 1st International Workshop on Language Technology Platforms

We present a workflow manager for the flexible creation and customisation of NLP processing pipelines. The workflow manager addresses challenges in interoperability across various different NLP tasks and hardware-based resource usage. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on its custom definition language, explaining the communication components and the general system architecture and setup. We currently implement the system, which is grounded and motivated by real-world industry use cases in several innovation and transfer projects.

pdf bib
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
Georg Rehm | Dimitris Galanis | Penny Labropoulou | Stelios Piperidis | Martin Welß | Ricardo Usbeck | Joachim Köhler | Miltos Deligiannis | Katerina Gkirtzou | Johannes Fischer | Christian Chiarcos | Nils Feldhus | Julian Moreno-Schneider | Florian Kintzel | Elena Montiel | Víctor Rodríguez Doncel | John Philip McCrae | David Laqua | Irina Patricia Theile | Christian Dittmar | Kalina Bontcheva | Ian Roberts | Andrejs Vasiļjevs | Andis Lagzdiņš
Proceedings of the 1st International Workshop on Language Technology Platforms

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.

pdf bib
Orchestrating NLP Services for the Legal Domain
Julian Moreno-Schneider | Georg Rehm | Elena Montiel-Ponsoda | Víctor Rodriguez-Doncel | Artem Revenko | Sotirios Karampatakis | Maria Khvalchik | Christian Sageder | Jorge Gracia | Filippo Maganza
Proceedings of the Twelfth Language Resources and Evaluation Conference

Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions.

pdf bib
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm | Katrin Marheinecke | Stefanie Hegele | Stelios Piperidis | Kalina Bontcheva | Jan Hajič | Khalid Choukri | Andrejs Vasiļjevs | Gerhard Backfried | Christoph Prinz | José Manuel Gómez-Pérez | Luc Meertens | Paul Lukowicz | Josef van Genabith | Andrea Lösch | Philipp Slusallek | Morten Irgens | Patrick Gatellier | Joachim Köhler | Laure Le Bars | Dimitra Anastasiou | Albina Auksoriūtė | Núria Bel | António Branco | Gerhard Budin | Walter Daelemans | Koenraad De Smedt | Radovan Garabík | Maria Gavriilidou | Dagmar Gromann | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Jan Odijk | Maciej Ogrodniczuk | Eiríkur Rögnvaldsson | Mike Rosner | Bolette Pedersen | Inguna Skadiņa | Marko Tadić | Dan Tufiș | Tamás Váradi | Kadri Vider | Andy Way | François Yvon
Proceedings of the Twelfth Language Resources and Evaluation Conference

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

pdf bib
European Language Grid: An Overview
Georg Rehm | Maria Berger | Ela Elsholz | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Stelios Piperidis | Miltos Deligiannis | Dimitris Galanis | Katerina Gkirtzou | Penny Labropoulou | Kalina Bontcheva | David Jones | Ian Roberts | Jan Hajič | Jana Hamrlová | Lukáš Kačena | Khalid Choukri | Victoria Arranz | Andrejs Vasiļjevs | Orians Anvari | Andis Lagzdiņš | Jūlija Meļņika | Gerhard Backfried | Erinç Dikici | Miroslav Janosik | Katja Prinz | Christoph Prinz | Severin Stampler | Dorothea Thomas-Aniola | José Manuel Gómez-Pérez | Andres Garcia Silva | Christian Berrío | Ulrich Germann | Steve Renals | Ondrej Klejch
Proceedings of the Twelfth Language Resources and Evaluation Conference

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.

pdf bib
Making Metadata Fit for Next Generation Language Technology Platforms: The Metadata Schema of the European Language Grid
Penny Labropoulou | Katerina Gkirtzou | Maria Gavriilidou | Miltos Deligiannis | Dimitris Galanis | Stelios Piperidis | Georg Rehm | Maria Berger | Valérie Mapelli | Michael Rigault | Victoria Arranz | Khalid Choukri | Gerhard Backfried | José Manuel Gómez-Pérez | Andres Garcia-Silva
Proceedings of the Twelfth Language Resources and Evaluation Conference

The current scientific and technological landscape is characterised by the increasing availability of data resources and processing tools and services. In this setting, metadata have emerged as a key factor facilitating management, sharing and usage of such digital assets. In this paper we present ELG-SHARE, a rich metadata schema catering for the description of Language Resources and Technologies (processing and generation services and tools, models, corpora, term lists, etc.), as well as related entities (e.g., organizations, projects, supporting documents, etc.). The schema powers the European Language Grid platform that aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. ELG-SHARE has been based on various metadata schemas, vocabularies, and ontologies, as well as related recommendations and guidelines.

pdf bib
A Dataset of German Legal Documents for Named Entity Recognition
Elena Leitner | Georg Rehm | Julian Moreno-Schneider
Proceedings of the Twelfth Language Resources and Evaluation Conference

We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.

pdf bib
Named Entities in Medical Case Reports: Corpus and Experiments
Sarah Schulz | Jurica Ševa | Samuel Rodriguez | Malte Ostendorff | Georg Rehm
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central’s open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.

pdf bib
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
Dmitrii Aksenov | Julian Moreno-Schneider | Peter Bourgonje | Robert Schwarzenberg | Leonhard Hennig | Georg Rehm
Proceedings of the Twelfth Language Resources and Evaluation Conference

We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. We also explore how locality modeling, i.e., the explicit restriction of calculations to the local context, can affect the summarization ability of the Transformer. This is done by introducing 2-dimensional convolutional self-attention into the first layers of the encoder. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset. We additionally train our model on the SwissText dataset to demonstrate usability on German. Both models outperform the baseline in ROUGE scores on two datasets and show its superiority in a manual qualitative analysis.

pdf bib
Aspect-based Document Similarity for Research Papers
Malte Ostendorff | Terry Ruas | Till Blume | Bela Gipp | Georg Rehm
Proceedings of the 28th International Conference on Computational Linguistics

Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like recommender systems that rely on document similarity. In this paper, we extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity approach for research papers. Paper citations indicate the aspect-based similarity, i.e., the title of a section in which a citation occurs acts as a label for the pair of citing and cited paper. We apply a series of Transformer models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. According to our results, SciBERT is the best performing system with F1-scores of up to 0.83. A qualitative analysis validates our quantitative results and indicates that aspect-based document similarity indeed leads to more fine-grained recommendations.

2019

pdf bib
Developing and Orchestrating a Portfolio of Natural Legal Language Processing and Document Curation Services
Georg Rehm | Julián Moreno-Schneider | Jorge Gracia | Artem Revenko | Victor Mireles | Maria Khvalchik | Ilan Kernerman | Andis Lagzdins | Marcis Pinnis | Artus Vasilevskis | Elena Leitner | Jan Milde | Pia Weißenhorn
Proceedings of the Natural Legal Language Processing Workshop 2019

We present a portfolio of natural legal language processing and document curation services currently under development in a collaborative European project. First, we give an overview of the project and the different use cases, while, in the main part of the article, we focus upon the 13 different processing services that are being deployed in different prototype applications using a flexible and scalable microservices architecture. Their orchestration is operationalised using a content and document curation workflow manager.

2018

pdf bib
Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena
Georg Rehm | Julian Moreno-Schneider | Peter Bourgonje
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Language Technology for Multilingual Europe: An Analysis of a Large-Scale Survey regarding Challenges, Demands, Gaps and Needs
Georg Rehm | Stefanie Hegele
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

pdf bib
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics
Ankit Srivastava | Georg Rehm | Julian Moreno Schneider
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) participated in two subtasks: Subtask A (determining the stance of a message) and Subtask B (determining veracity of a message, closed variant). In both cases, our implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules (heuristics) applied in a post-process cascading fashion. We provide a detailed analysis of the system performance and report on variants of our systems that were not part of the official submission.

pdf bib
Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters
Georg Rehm | Julian Moreno Schneider | Peter Bourgonje | Ankit Srivastava | Jan Nehring | Armin Berger | Luca König | Sören Räuchle | Jens Gerth
Proceedings of the Events and Stories in the News Workshop

We present an approach at identifying a specific class of events, movement action events (MAEs), in a data set that consists of ca. 2,800 personal letters exchanged by the German architect Erich Mendelsohn and his wife, Luise. A backend system uses these and other semantic analysis results as input for an authoring environment that digital curators can use to produce new pieces of digital content. In our example case, the human expert will receive recommendations from the system with the goal of putting together a travelogue, i.e., a description of the trips and journeys undertaken by the couple. We describe the components and architecture and also apply the system to news data.

pdf bib
Semantic Storytelling, Cross-lingual Event Detection and other Semantic Services for a Newsroom Content Curation Dashboard
Julian Moreno-Schneider | Ankit Srivastava | Peter Bourgonje | David Wabnitz | Georg Rehm
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

We present a prototypical content curation dashboard, to be used in the newsroom, and several of its underlying semantic content analysis components (such as named entity recognition, entity linking, summarisation and temporal expression analysis). The idea is to enable journalists (a) to process incoming content (agency reports, twitter feeds, reports, blog posts, social media etc.) and (b) to create new articles more easily and more efficiently. The prototype system also allows the automatic annotation of events in incoming content for the purpose of supporting journalists in identifying important, relevant or meaningful events and also to adapt the content currently in production accordingly in a semi-automatic way. One of our long-term goals is to support journalists building up entire storylines with automatic means. In the present prototype they are generated in a backend service using clustering methods that operate on the extracted events.

pdf bib
From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles
Peter Bourgonje | Julian Moreno Schneider | Georg Rehm
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information. We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 89.59.

pdf bib
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Martin Popel | Milan Straka | Jan Hajič | Joakim Nivre | Filip Ginter | Juhani Luotolahti | Sampo Pyysalo | Slav Petrov | Martin Potthast | Francis Tyers | Elena Badmaeva | Memduh Gokirmak | Anna Nedoluzhko | Silvie Cinková | Jan Hajič jr. | Jaroslava Hlaváčová | Václava Kettnerová | Zdeňka Urešová | Jenna Kanerva | Stina Ojala | Anna Missilä | Christopher D. Manning | Sebastian Schuster | Siva Reddy | Dima Taji | Nizar Habash | Herman Leung | Marie-Catherine de Marneffe | Manuela Sanguinetti | Maria Simi | Hiroshi Kanayama | Valeria de Paiva | Kira Droganova | Héctor Martínez Alonso | Çağrı Çöltekin | Umut Sulubacak | Hans Uszkoreit | Vivien Macketanz | Aljoscha Burchardt | Kim Harris | Katrin Marheinecke | Georg Rehm | Tolga Kayadelen | Mohammed Attia | Ali Elkahky | Zhuoran Yu | Emily Pitler | Saran Lertpradit | Michael Mandl | Jesse Kirchner | Hector Fernandez Alcalde | Jana Strnadová | Esha Banerjee | Ruli Manurung | Antonio Stella | Atsuko Shimada | Sookyoung Kwak | Gustavo Mendonça | Tatiana Lando | Rattima Nitisaroj | Josie Li
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

2016

pdf bib
Digital curation technologies (DKT)
Georg Rehm | Felix Sasaki
Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products

pdf bib
CRACKER – cracking the language barrier. Selected results 2015/2016
Georg Rehm
Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products

pdf bib
How to configure statistical machine translation with linked open data resources
Ankit Srivastava | Felix Sasaki | Peter Bourgonje. Julian Moreno-Schneider | Jan Nehring | Georg Rehm
Proceedings of Translating and the Computer 38

pdf bib
Fostering the Next Generation of European Language Technology: Recent Developments ― Emerging Initiatives ― Challenges and Opportunities
Georg Rehm | Jan Hajič | Josef van Genabith | Andrejs Vasiljevs
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

META-NET is a European network of excellence, founded in 2010, that consists of 60 research centres in 34 European countries. One of the key visions and goals of META-NET is a truly multilingual Europe, which is substantially supported and realised through language technologies. In this article we provide an overview of recent developments around the multilingual Europe topic, we also describe recent and upcoming events as well as recent and upcoming strategy papers. Furthermore, we provide overviews of two new emerging initiatives, the CEF.AT and ELRC activity on the one hand and the Cracking the Language Barrier federation on the other. The paper closes with several suggested next steps in order to address the current challenges and to open up new opportunities.

pdf bib
The Language Resource Life Cycle: Towards a Generic Model for Creating, Maintaining, Using and Distributing Language Resources
Georg Rehm
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Language Resources (LRs) are an essential ingredient of current approaches in Linguistics, Computational Linguistics, Language Technology and related fields. LRs are collections of spoken or written language data, typically annotated with linguistic analysis information. Different types of LRs exist, for example, corpora, ontologies, lexicons, collections of spoken language data (audio), or collections that also include video (multimedia, multimodal). Often, LRs are distributed with specific tools, documentation, manuals or research publications. The different phases that involve creating and distributing an LR can be conceptualised as a life cycle. While the idea of handling the LR production and maintenance process in terms of a life cycle has been brought up quite some time ago, a best practice model or common approach can still be considered a research gap. This article wants to help fill this gap by proposing an initial version of a generic Language Resource Life Cycle that can be used to inform, direct, control and evaluate LR research and development activities (including description, management, production, validation and evaluation workflows).

pdf bib
Processing Document Collections to Automatically Extract Linked Data: Semantic Storytelling Technologies for Smart Curation Workflows
Peter Bourgonje | Julian Moreno Schneider | Georg Rehm | Felix Sasaki
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

2015

pdf bib
CRACKER: Cracking the Language Barrier
Georg Rehm
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

pdf bib
CRACKER: Cracking the Language Barrier
Georg Rehm
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

2014

pdf bib
The Strategic Impact of META-NET on the Regional, National and International Level
Georg Rehm | Hans Uszkoreit | Sophia Ananiadou | Núria Bel | Audronė Bielevičienė | Lars Borin | António Branco | Gerhard Budin | Nicoletta Calzolari | Walter Daelemans | Radovan Garabík | Marko Grobelnik | Carmen García-Mateo | Josef van Genabith | Jan Hajič | Inma Hernáez | John Judge | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Joseph Mariani | John McNaught | Maite Melero | Monica Monachini | Asunción Moreno | Jan Odijk | Maciej Ogrodniczuk | Piotr Pęzik | Stelios Piperidis | Adam Przepiórkowski | Eiríkur Rögnvaldsson | Michael Rosner | Bolette Pedersen | Inguna Skadiņa | Koenraad De Smedt | Marko Tadić | Paul Thompson | Dan Tufiş | Tamás Váradi | Andrejs Vasiļjevs | Kadri Vider | Jolanta Zabarskaite
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.

pdf bib
META-SHARE: One year after
Stelios Piperidis | Harris Papageorgiou | Christian Spurk | Georg Rehm | Khalid Choukri | Olivier Hamon | Nicoletta Calzolari | Riccardo del Gratta | Bernardo Magnini | Christian Girardi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents META-SHARE (www.meta-share.eu), an open language resource infrastructure, and its usage since its Europe-wide deployment in early 2013. META-SHARE is a network of repositories that store language resources (data, tools and processing services) documented with high-quality metadata, aggregated in central inventories allowing for uniform search and access. META-SHARE was developed by META-NET (www.meta-net.eu) and aims to serve as an important component of a language technology marketplace for researchers, developers, professionals and industrial players, catering for the full development cycle of language technology, from research through to innovative products and services. The observed usage in its initial steps, the steadily increasing number of network nodes, resources, users, queries, views and downloads are all encouraging and considered as supportive of the choices made so far. In tandem, take-up activities like direct linking and processing of datasets by language processing services as well as metadata transformation to RDF are expected to open new avenues for data and resources linking and boost the organic growth of the infrastructure while facilitating language technology deployment by much wider research communities and industrial sectors.

2013

pdf bib
MATECAT: Machine Translation Enhanced Computer Assisted Translation META - Multilingual Europe Technology Alliance
Georg Rehm | Aljoscha Burchardt | Felix Sasaki
Proceedings of Machine Translation Summit XIV: European projects

pdf bib
META - Multilingual Europe Technology Alliance
Georg Rehm | Aljoscha Burchardt | Felix Sasaki
Proceedings of Machine Translation Summit XIV: European projects

2008

pdf bib
Towards a Reference Corpus of Web Genres for the Evaluation of Genre Identification Systems
Georg Rehm | Marina Santini | Alexander Mehler | Pavel Braslavski | Rüdiger Gleim | Andrea Stubbe | Svetlana Symonenko | Mirko Tavosanis | Vedrana Vidulin
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present initial results from an international and multi-disciplinary research collaboration that aims at the construction of a reference corpus of web genres. The primary application scenario for which we plan to build this resource is the automatic identification of web genres. Web genres are rather difficult to capture and to describe in their entirety, but we plan for the finished reference corpus to contain multi-level tags of the respective genre or genres a web document or a website instantiates. As the construction of such a corpus is by no means a trivial task, we discuss several alternatives that are, for the time being, mostly based on existing collections. Furthermore, we discuss a shared set of genre categories and a multi-purpose tool as two additional prerequisites for a reference corpus of web genres.

pdf bib
Ontology-Based XQuery’ing of XML-Encoded Language Resources on Multiple Annotation Layers
Georg Rehm | Richard Eckart | Christian Chiarcos | Johannes Dellert
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present an approach for querying collections of heterogeneous linguistic corpora that are annotated on multiple layers using arbitrary XML-based markup languages. An OWL ontology provides a homogenising view on the conceptually different markup languages so that a common querying framework can be established using the method of ontology-based query expansion. In addition, we present a highly flexible web-based graphical interface that can be used to query corpora with regard to several different linguistic properties such as, for example, syntactic tree fragments. This interface can also be used for ontology-based querying of multiple corpora simultaneously.

pdf bib
The Metadata-Database of a Next Generation Sustainability Web-Platform for Language Resources
Georg Rehm | Oliver Schonefeld | Andreas Witt | Timm Lehmberg | Christian Chiarcos | Hanan Bechara | Florian Eishold | Kilian Evang | Magdalena Leshtanska | Aleksandar Savkov | Matthias Stark
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Our goal is to provide a web-based platform for the long-term preservation and distribution of a heterogeneous collection of linguistic resources. We discuss the corpus preprocessing and normalisation phase that results in sets of multi-rooted trees. At the same time we transform the original metadata records, just like the corpora annotated using different annotation approaches and exhibiting different levels of granularity, into the all-encompassing and highly flexible format eTEI for which we present editing and parsing tools. We also discuss the architecture of the sustainability platform. Its primary components are an XML database that contains corpus and metadata files and an SQL database that contains user accounts and access control lists. A staging area, whose structure, contents, and consistency can be checked using tools, is used to make sure that new resources about to be imported into the platform have the correct structure.
Search
Co-authors