no code implementations • ACL 2017 • Ines Rehbein, Josef Ruppenhofer
We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost.
no code implementations • WS 2017 • Ines Rehbein, Josef Ruppenhofer
In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus.
no code implementations • WS 2017 • Bich-Ngoc Do, Ines Rehbein, Anette Frank
We propose a new type of subword embedding designed to provide more information about unknown compounds, a major source for OOV words in German.
no code implementations • WS 2017 • Julian Hitschler, Esther van den Berg, Ines Rehbein
We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications.
no code implementations • WS 2017 • Bich-Ngoc Do, Ines Rehbein
To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history.
no code implementations • COLING 2018 • Ines Rehbein, Josef Ruppenhofer
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning.
no code implementations • LREC 2014 • Ines Rehbein, S{\"o}ren Schalowski, Heike Wiese
This paper presents the first release of the KiezDeutsch Korpus (KiDKo), a new language resource with multiparty spoken dialogues of Kiezdeutsch, a newly emerging language variety spoken by adolescents from multiethnic urban areas in Germany.
no code implementations • LREC 2012 • Josef Ruppenhofer, Ines Rehbein
This paper presents an annotation scheme for English modal verbs together with sense-annotated data from the news domain.
no code implementations • WS 2019 • Uli Steinbach, Ines Rehbein
This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language.
no code implementations • LREC 2016 • Ines Rehbein, Merel Scholman, Vera Demberg
In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data.
no code implementations • WS 2019 • Ines Rehbein
We present a corpus study where we control for speaker and topic and show that the distribution of different discourse connectives varies considerably across different discourse settings.
no code implementations • LREC 2020 • Josef Ruppenhofer, Ines Rehbein, Carolina Flinz
Building on the OntoNotes 5. 0 NER inventory, our scheme is adapted for a corpus of transcripts of biographic interviews by adding categories for AGE and LAN(guage) and also features extended numeric and temporal categories.
no code implementations • LREC 2020 • Manuela Sanguinetti, Cristina Bosco, Lauren Cassidy, {\"O}zlem {\c{C}}etino{\u{g}}lu, Aless Cignarella, ra Teresa, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djam{\'e} Seddah, Amir Zeldes
The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework.
no code implementations • LREC 2020 • Ines Rehbein, Josef Ruppenhofer
In the paper, we present inter-annotator agreement scores for our dataset and discuss problems for annotating causal language.
no code implementations • LREC 2020 • Ines Rehbein, Josef Ruppenhofer, Thomas Schmidt
For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem assentence pair classification task, as compared to a sequence tagging approach.
no code implementations • ACL 2020 • Bich-Ngoc Do, Ines Rehbein
We show that the GCN not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech.
no code implementations • 3 Nov 2020 • Manuela Sanguinetti, Lauren Cassidy, Cristina Bosco, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah, Amir Zeldes
This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis.
no code implementations • COLING 2020 • Bich-Ngoc Do, Ines Rehbein
In particular, we show that using gold information for the extraction of attachment candidates as well as a missing comparison of the system{'}s output to the output of a full syntactic parser leads to an overly optimistic assessment of the results.
no code implementations • UDW (COLING) 2020 • Josef Ruppenhofer, Ines Rehbein
We argue that a unified treatment of constructions across languages will increase the consistency of the UD annotations and thus the quality of the treebanks for linguistic analysis.
no code implementations • EMNLP 2021 • Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition.
no code implementations • KONVENS (WS) 2021 • Ines Rehbein, Josef Ruppenhofer, Julian Bernauer
This paper investigates the use of first person plural pronouns as a rhetorical device in political speeches.
no code implementations • LREC 2022 • Ines Rehbein, Josef Ruppenhofer
This paper presents a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, that is their ability to construct and manage in-groups and out-groups by including/excluding the audience and/or non-speech act participants in reference to groups that also include the speaker.
1 code implementation • CONLL 2019 • Raphael Schumann, Ines Rehbein
Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers.
1 code implementation • COLING (ArgMining) 2020 • Jonathan Kobbe, Ines Rehbein, Ioana Hulpuș, Heiner Stuckenschmidt
Sentiment and stance are two important concepts for the analysis of arguments.
1 code implementation • ParlaCLARIN (LREC) 2022 • Christopher Klamm, Ines Rehbein, Simone Paolo Ponzetto
In addition, we present a new annotated data set of parliamentary debates, following the coding schema of policy topics developed in the Comparative Agendas Project (CAP), and release models for topic classification in parliamentary debates.
2 code implementations • 12 Apr 2019 • Federico Nanni, Goran Glavas, Ines Rehbein, Simone Paolo Ponzetto, Heiner Stuckenschmidt
During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science.