klinger.bib
@inproceedings{greschner-klinger-2025-fearful,
title = {Fearful Falcons and Angry Llamas: Emotion Category
Annotations of Arguments by Humans and {LLM}s},
author = {Greschner, Lynn and Klinger, Roman},
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and {\"O}hman, Emily
and Bizzoni, Yuri and Miyagawa, So and Alnajjar,
Khalid},
booktitle = {Proceedings of the 5th International Conference on
Natural Language Processing for Digital Humanities},
month = may,
year = {2025},
address = {Albuquerque, USA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.nlp4dh-1.52/},
pages = {628--646},
isbn = {979-8-89176-234-3},
abstract = {Arguments evoke emotions, influencing the effect of
the argument itself. Not only the emotional
intensity but also the category influences the
argument`s effects, for instance, the willingness to
adapt stances. While binary emotionality has been
studied in argumentative texts, there is no work on
discrete emotion categories (e.g.,
{\textquoteleft}anger') in such data. To fill this
gap, we crowdsource subjective annotations of
emotion categories in a German argument corpus and
evaluate automatic LLM-based labeling
methods. Specifically, we compare three prompting
strategies (zero-shot, one-shot, chain-of-thought)
on three large instruction-tuned language models
(Falcon-7b-instruct, Llama-3.1-8B-instruct,
GPT-4o-mini). We further vary the definition of the
output space to be binary (is there emotionality in
the argument?), closed-domain (which emotion from a
given label set is in the argument?), or open-domain
(which emotion is in the argument?). We find that
emotion categories enhance the prediction of
emotionality in arguments, emphasizing the need for
discrete emotion annotations in arguments. Across
all prompt settings and models, automatic
predictions show a high recall but low precision for
predicting anger and fear, indicating a strong bias
toward negative emotions.},
url = {https://arxiv.org/abs/2412.15993},
eprint = {2412.15993},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
internaltype = {conferenceproc}
}
@inproceedings{menchaca-resendiz-klinger-2025-mopo,
title = {{MOPO}: Multi-Objective Prompt Optimization for Affective Text Generation},
author = {Menchaca Resendiz, Yarik and
Klinger, Roman},
editor = {Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven},
booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
month = jan,
year = {2025},
address = {Abu Dhabi, UAE},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.coling-main.375/},
pages = {5588--5606},
eprint = {2412.12948},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2412.12948},
internaltype = {conferenceproc},
abstract = {How emotions are expressed depends on the context and domain. On X (formerly Twitter), for instance, an author might simply use the hashtag {\#}anger, while in a news headline, emotions are typically written in a more polite, indirect manner. To enable conditional text generation models to create emotionally connotated texts that fit a domain, users need to have access to a parameter that allows them to choose the appropriate way to express an emotion. To achieve this, we introduce MOPO, a Multi-Objective Prompt Optimization methodology. MOPO optimizes prompts according to multiple objectives (which correspond here to the output probabilities assigned by emotion classifiers trained for different domains). In contrast to single objective optimization, MOPO outputs a set of prompts, each with a different weighting of the multiple objectives. Users can then choose the most appropriate prompt for their context. We evaluate MOPO using three objectives, determined by various domain-specific emotion classifiers. MOPO improves performance by up to 15 pp across all objectives with a minimal loss (1{--}2 pp) for any single objective compared to single-objective optimization. These minor performance losses are offset by a broader generalization across multiple objectives {--} which is not possible with single-objective optimization. Additionally, MOPO reduces computational requirements by simultaneously optimizing for multiple objectives, eliminating separate optimization procedures for each objective.}
}
@inproceedings{troiano-etal-2024-dealing,
title = {Dealing with Controversy: An Emotion and Coping
Strategy Corpus Based on Role Playing},
author = {Troiano, Enrica and Labat, Sofie and Stranisci,
Marco and Damiano, Rossana and Patti, Viviana and
Klinger, Roman},
editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen,
Yun-Nung},
booktitle = {Findings of the Association for Computational
Linguistics: EMNLP 2024},
month = nov,
year = {2024},
address = {Miami, Florida, USA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.findings-emnlp.89},
pages = {1634--1658},
abstract = {There is a mismatch between psychological and
computational studies on emotions. Psychological
research aims at explaining and documenting internal
mechanisms of these phenomena, while computational
work often simplifies them into labels. Many emotion
fundamentals remain under-explored in natural
language processing, particularly how emotions
develop and how people cope with them. To help
reduce this gap, we follow theories on coping, and
treat emotions as strategies to cope with salient
situations (i.e., how people deal with
emotion-eliciting events). This approach allows us
to investigate the link between emotions and
behavior, which also emerges in language. We
introduce the task of coping identification,
together with a corpus to do so, constructed via
role-playing. We find that coping strategies realize
in text even though they are challenging to
recognize, both for humans and automatic systems
trained and prompted on the same task. We thus open
up a promising research direction to enhance the
capability of models to better capture emotion
mechanisms from text.},
internaltype = {conferenceproc},
pdf = {https://www.romanklinger.de/publications/TroianoLabatStranisciDamianoPattiKlinger_EMNLP-Findings2024.pdf},
eprint = {2409.19025},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2409.19025}
}
@inproceedings{velutharambath-etal-2024-entangled,
title = {How Entangled is Factuality and Deception in
{G}erman?},
author = {Velutharambath, Aswathy and Wuehrl, Amelie and
Klinger, Roman},
editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen,
Yun-Nung},
booktitle = {Findings of the Association for Computational
Linguistics: EMNLP 2024},
month = nov,
year = {2024},
address = {Miami, Florida, USA},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.findings-emnlp.557},
pages = {9538--9554},
abstract = {The statement {``}The earth is flat{''} is factually
inaccurate, but if someone truly believes and argues
in its favor, it is not deceptive. Research on
deception detection and fact checking often
conflates factual accuracy with the truthfulness of
statements. This assumption makes it difficult to
(a) study subtle distinctions and interactions
between the two and (b) gauge their effects on
downstream tasks. The belief-based deception
framework disentangles these properties by defining
texts as deceptive when there is a mismatch between
what people say and what they truly believe. In this
study, we assess if presumed patterns of deception
generalize to German language texts. We test the
effectiveness of computational models in detecting
deception using an established corpus of
belief-based argumentation. Finally, we gauge the
impact of deception on the downstream task of fact
checking and explore if this property confounds
verification models. Surprisingly, our analysis
finds no correlation with established cues of
deception. Previous work claimed that computational
models can outperform humans in deception detection
accuracy, however, our experiments show that both
traditional and state-of-the-art models struggle
with the task, performing no better than random
guessing. For fact checking, we find that natural
language inference-based verification performs worse
on non-factual and deceptive content, while
prompting large language models for the same task is
less sensitive to these properties.},
internaltype = {conferenceproc},
pdf = {https://www.romanklinger.de/publications/VelutharambathWuehrlKlinger-EMNLP-Findings2024.pdf},
eprint = {2409.20165},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2409.20165}
}
@inproceedings{bagdon-etal-2024-expert,
title = {{``}You are an expert annotator{''}: Automatic Best{--}Worst-Scaling Annotations for Emotion Intensity Modeling},
author = {Bagdon, Christopher and
Karmalkar, Prathamesh and
Gurulingappa, Harsha and
Klinger, Roman},
editor = {Duh, Kevin and
Gomez, Helena and
Bethard, Steven},
booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
month = jun,
year = {2024},
address = {Mexico City, Mexico},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.naacl-long.439},
pages = {7917--7929},
abstract = {Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly for categorical annotations. Some NLP tasks such as emotion intensity prediction, however, require text regression, but there is no work on automating annotations for continuous label assignments. Regression is considered more challenging than classification: The fact that humans perform worse when tasked to choose values from a rating scale lead to comparative annotation methods, including best{--}worst scaling. This raises the question if large language model-based annotation methods show similar patterns, namely that they perform worse on rating scale annotation tasks than on comparative annotation tasks. To study this, we automate emotion intensity predictions and compare direct rating scale predictions, pairwise comparisons and best{--}worst scaling. We find that the latter shows the highest reliability. A transformer regressor fine-tuned on these data performs nearly on par with a model trained on the original manual annotations.},
internaltype = {conferenceproc},
url = {https://www.romanklinger.de/publications/BagdonNAACL2024.pdf}
}
@inproceedings{Wuehrl2024b,
title = {Understanding Fine-grained Distortions in Reports of Scientific Findings},
author = {Wuehrl, Amelie and
Wright, Dustin and
Klinger, Roman and
Augenstein, Isabelle},
editor = {Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek},
booktitle = {Findings of the Association for Computational Linguistics ACL 2024},
month = aug,
year = {2024},
address = {Bangkok, Thailand and virtual meeting},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.findings-acl.369},
pages = {6175--6191},
abstract = {Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public, and methods to detect distortions from the original work automatically, are crucial. Prior work focused on individual aspects of distortions or worked with unpaired data. In this work, we make three foundational contributions towards addressing this problem: (1) annotating 1,600 instances of scientific findings from academic papers paired with corresponding findings as reported in news articles and tweets wrt. four characteristics: causality, certainty, generality and sensationalism; (2) establishing baselines for automatically detecting these characteristics; and (3) analyzing the prevalence of changes in these characteristics in both human-annotated and large-scale unlabeled data. Our results show that scientific findings frequently undergo subtle distortions when reported. Tweets distort findings more often than science news reports. Detecting fine-grained distortions automatically poses a challenging task. In our experiments, fine-tuned task-specific models consistently outperform few-shot LLM prompting.},
pdf = {https://www.romanklinger.de/publications/WuehrlEtAlACLFindings2024.pdf},
archiveprefix = {arXiv},
eprint = {2402.12431},
internaltype = {conferenceproc}
}
@inproceedings{Wemmer2024,
title = {{E}mo{P}rogress: Cumulated Emotion Progression
Analysis in Dreams and Customer Service Dialogues},
author = {Wemmer, Eileen and Labat, Sofie and Klinger, Roman},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste,
Veronique and Lenci, Alessandro and Sakti, Sakriani
and Xue, Nianwen},
booktitle = {Proceedings of the 2024 Joint International
Conference on Computational Linguistics, Language
Resources and Evaluation (LREC-COLING 2024)},
month = may,
year = {2024},
address = {Torino, Italy},
publisher = {ELRA and ICCL},
url = {https://aclanthology.org/2024.lrec-main.503},
pages = {5660--5677},
pdf = {https://www.romanklinger.de/publications/WemmerLabatKlingerLRECCOLING2024.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Velutharambath2024,
title = {Can Factual Statements Be Deceptive? The
{D}e{F}a{B}el Corpus of Belief-based Deception},
author = {Velutharambath, Aswathy and W{\"u}hrl, Amelie and
Klinger, Roman},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste,
Veronique and Lenci, Alessandro and Sakti, Sakriani
and Xue, Nianwen},
booktitle = {Proceedings of the 2024 Joint International
Conference on Computational Linguistics, Language
Resources and Evaluation (LREC-COLING 2024)},
month = may,
year = {2024},
address = {Torino, Italy},
publisher = {ELRA and ICCL},
url = {https://aclanthology.org/2024.lrec-main.243},
pages = {2708--2723},
internaltype = {conferenceproc},
pdf = {https://www.romanklinger.de/publications/VelutharambathWuehrlKlinger-LREC-COLING2024.pdf},
eprint = {2403.10185},
archiveprefix = {arXiv},
primaryclass = {cs.CL}
}
@inproceedings{wuehrl-etal-2024-makes,
title = {What Makes Medical Claims (Un)Verifiable? Analyzing
Entity and Relation Properties for Fact
Verification},
author = {Wührl, Amelie and Menchaca Resendiz, Yarik and
Grimminger, Lara and Klinger, Roman},
editor = {Graham, Yvette and Purver, Matthew},
booktitle = {Proceedings of the 18th Conference of the European
Chapter of the Association for Computational
Linguistics (Volume 1: Long Papers)},
month = mar,
year = {2024},
address = {St. Julian{'}s, Malta},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.eacl-long.124},
pages = {2046--2058},
abstract = {Verifying biomedical claims fails if no evidence can
be discovered. In these cases, the fact-checking
verdict remains unknown and the claim is
unverifiable. To improve this situation, we have to
understand if there are any claim properties that
impact its verifiability. In this work we assume
that entities and relations define the core
variables in a biomedical claim{'}s anatomy and
analyze if their properties help us to differentiate
verifiable from unverifiable claims. In a study with
trained annotation experts we prompt them to find
evidence for biomedical claims, and observe how they
refine search queries for their evidence
search. This leads to the first corpus for
scientific fact verification annotated with
subject{--}relation{--}object triplets, evidence
documents, and fact-checking verdicts (the BEAR-FACT
corpus). We find (1) that discovering evidence for
negated claims (e.g., X{--}does-not-cause{--}Y) is
particularly challenging. Further, we see that
annotators process queries mostly by adding
constraints to the search and by normalizing
entities to canonical names. (2) We compare our
in-house annotations with a small crowdsourcing
setting where we employ both medical experts and
laypeople. We find that domain expertise does not
have a substantial effect on the reliability of
annotations. Finally, (3), we demonstrate that it is
possible to reliably estimate the success of
evidence retrieval purely from the claim text
(.82F$_1$), whereas identifying unverifiable claims
proves more challenging (.27F$_1$)},
pdf = {https://www.romanklinger.de/publications/Wuehrl-etal-2024-EACL.pdf},
eprint = {2402.01360},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
internaltype = {conferenceproc}
}
@inproceedings{MenchacaResendiz2023,
title = {Affective Natural Language Generation of Event
Descriptions through Fine-grained Appraisal
Conditions},
author = {Menchaca Resendiz, Yarik and Klinger, Roman},
booktitle = {Proceedings of the 16th International Conference on
Natural Language Generation},
month = sep,
year = {2023},
address = {Prague, Czech Republic},
publisher = {Association for Computational Linguistics},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2307.14004},
url = {https://aclanthology.org/2023.inlg-1.26},
pdf = {https://www.romanklinger.de/publications/MenchacaResendiz_Klinger_INLG2023.pdf}
}
@incollection{Klinger2023,
address = {Berlin, Heidelberg},
title = {Emotionsklassifikation in {Texten} unter
{Berücksichtigung} des {Komponentenprozessmodells}},
isbn = {978-3-662-65963-2 978-3-662-65964-9},
url = {https://link.springer.com/10.1007/978-3-662-65964-9_7},
abstract = {Zusammenfassung Ein wichtiger Bestandteil unserer
alltäglichen Kommunikation, neben der Mitteilung und
Beschreibung von Ereignissen und Fakten, ist der
Ausdruck von Emotionen, welcher auch Bestandteil von
Hassrede ist: Es wird zum Beispiel Wut zum Ausdruck
gebracht, was wiederum bei den Betroffenen Angst,
Traurigkeit oder vielleicht auch Überraschung
auslösen kann. In der maschinellen Verarbeitung von
Sprache haben sich in der letzten Zeit einige
konkrete Aufgaben, welche Teil der Emotionsanalyse
in Text sind, herauskristallisiert. Diese sind zum
einen Klassifikationsaufgaben (welche Emotion drückt
ein Text aus?) und zum anderen relationale
Strukturlernaufgaben (welche Wörter bezeichnen die
Person, die eine Emotion fühlt und welche Wörter
lassen auf die Ursache der Emotion schließen?). Wir
verschaffen uns in diesem Kapitel einen kurzen
Überblick über das Feld und diskutieren im Anschluss
etwas genauer, wie sich die Beschreibungen von
Emotionen in verschiedenen Domänen unterscheiden und
wie Ereignisbeschreibungen mit Hilfe psychologischer
Theorien mit Emotionen zusammengebracht werden
können. Insbesondere analysieren wir auf Basis des
Emotions-Komponenten-Prozessmodells, auf welche
Komponenten von Emotionen (subjektives Gefühl,
kognitive Evaluation, Körperreaktion, Ausdruck,
Motivation) Autor:innen zugreifen, und stellen fest,
dass diese Verteilung zwischen sozialen Medien und
Literatur unterschiedlich ist. In beiden Domänen
spielt aber die kognitive Komponente zur
Interpretation von Emotionen eine wichtige
Rolle. Dies zeigt auf, dass insbesondere der
Ereignisinterpretation Aufmerksamkeit geschenkt
werden muss, um implizit kommunizierte Emotionen
aufzudecken. Dies motiviert uns, Emotionen mit Hilfe
der Appraisaltheorien zu analysieren, welche den
Zusammenhang zwischen kognitiven Prozessen und
Emotionen erklären. Zu beiden Konzepten – dem
Komponentenmodell und den Appraisaltheorien –
präsentieren wir Textkorpora und
Klassifikationsmodelle.},
language = {de},
booktitle = {Digitale {Hate} {Speech}},
publisher = {Springer Berlin Heidelberg},
author = {Klinger, Roman},
editor = {Jaki, Sylvia and Steiger, Stefan},
year = {2023},
doi = {10.1007/978-3-662-65964-9_7},
pages = {131--154},
internaltype = {conferenceproc}
}
@inproceedings{Plazadelarco2022,
title = {Natural Language Inference Prompts for Zero-shot
Emotion Classification in Text across Corpora},
author = {Plaza-del-Arco, Flor Miriam and
Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa and Klinger,
Roman},
booktitle = {Proceedings of the 29th International Conference on
Computational Linguistics},
month = oct,
year = {2022},
address = {Gyeongju, Republic of Korea},
publisher = {International Committee on Computational
Linguistics},
url = {https://aclanthology.org/2022.coling-1.592},
pdf = {https://www.romanklinger.de/publications/PlazaDelArcoMartinValdiviaKlinger.pdf},
archiveprefix = {arXiv},
eprint = {2209.06701},
pages = {6805--6817},
abstract = {Within textual emotion classification, the set of
relevant labels depends on the domain and
application scenario and might not be known at the
time of model development. This conflicts with the
classical paradigm of supervised learning in which
the labels need to be predefined. A solution to
obtain a model with a flexible set of labels is to
use the paradigm of zero-shot learning as a natural
language inference task, which in addition adds the
advantage of not needing any labeled training
data. This raises the question how to prompt a
natural language inference model for zero-shot
learning emotion classification. Options for prompt
formulations include the emotion name anger alone or
the statement {``}This text expresses
anger{''}. With this paper, we analyze how sensitive
a natural language inference-based
zero-shot-learning classifier is to such changes to
the prompt under consideration of the corpus: How
carefully does the prompt need to be selected? We
perform experiments on an established set of emotion
datasets presenting different language registers
according to different sources (tweets, events,
blogs) with three natural language inference models
and show that indeed the choice of a particular
prompt formulation needs to fit to the corpus. We
show that this challenge can be tackled with
combinations of multiple prompts. Such ensemble is
more robust across corpora than individual prompts
and shows nearly the same performance as the
individual best prompt for a particular corpus.},
internaltype = {conferenceproc}
}
@inproceedings{mohr-whrl-klinger:2022:LREC,
author = {Mohr, Isabelle and W\"uhrl, Amelie and Klinger,
Roman},
title = {CoVERT: A Corpus of Fact-checked Biomedical COVID-19
Tweets},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {244--257},
abstract = {During the first two years of the COVID-19 pandemic,
large volumes of biomedical information concerning
this new disease have been published on social
media. Some of this information can pose a real
danger, particularly when false information is
shared, for instance recommendations how to treat
diseases without professional medical
advice. Therefore, automatic fact-checking resources
and systems developed specifically for medical
domain are crucial. While existing fact-checking
resources cover COVID-19 related information in news
or quantify the amount of misinformation in tweets,
there is no dataset providing fact-checked COVID-19
related Twitter posts with detailed annotations for
biomedical entities, relations and relevant
evidence. We contribute CoVERT, a fact-checked
corpus of tweets with a focus on the domain of
biomedicine and COVID-19 related
(mis)information. The corpus consists of 300 tweets,
each annotated with named entities and relations. We
employ a novel crowdsourcing methodology to annotate
all tweets with fact-checking labels and supporting
evidence, which crowdworkers search for online. This
methodology results in substantial inter-annotator
agreement. Furthermore, we use the retrieved
evidence extracts as part of a fact-checking
pipeline, finding that the real-world evidence is
more useful than the knowledge directly available in
pretrained language models.},
url = {https://aclanthology.org/2022.lrec-1.26},
internaltype = {conferenceproc},
pdf = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.26.pdf},
archiveprefix = {arXiv},
eprint = {2204.12164}
}
@inproceedings{whrl-klinger:2022:LREC,
author = {W\"uhrl, Amelie and Klinger, Roman},
title = {Recovering Patient Journeys: A Corpus of Biomedical
Entities and Relations on Twitter (BEAR)},
booktitle = {Proceedings of the Language Resources and Evaluation
Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {4439--4450},
abstract = {Text mining and information extraction for the
medical domain has focused on scientific text
generated by researchers. However, their access to
individual patient experiences or patient-doctor
interactions is limited. On social media, doctors,
patients and their relatives also discuss medical
information. Individual information provided by
laypeople complements the knowledge available in
scientific text. It reflects the patient's journey
making the value of this type of data twofold: It
offers direct access to people's perspectives, and
it might cover information that is not available
elsewhere, including self-treatment or
self-diagnose. Named entity recognition and relation
extraction are methods to structure information that
is available in unstructured text. However, existing
medical social media corpora focused on a comparably
small set of entities and relations. In contrast, we
provide rich annotation layers to model patients'
experiences in detail. The corpus consists of
medical tweets annotated with a fine-grained set of
medical entities and relations between them, namely
14 entity (incl. environmental factors, diagnostics,
biochemical processes, patients' quality-of-life
descriptions, pathogens, medical conditions, and
treatments) and 20 relation classes (incl. prevents,
influences, interactions, causes). The dataset
consists of 2,100 tweets with approx. 6,000 entities
and 2,200 relations.},
url = {https://aclanthology.org/2022.lrec-1.472},
internaltype = {conferenceproc},
pdf = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.472.pdf},
archiveprefix = {arXiv},
eprint = {2204.09952}
}
@inproceedings{troiano-EtAl:2022:LREC,
author = {Troiano, Enrica and Oberlaender, Laura Ana Maria and Wegge, Maximilian and Klinger, Roman},
title = {x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {1365--1375},
abstract = {Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. So far, this task has been the recognition of the emotion of writers and readers, as well as that of entities mentioned in the text. We argue that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. Based on appraisal theories in psychology, which treat emotions as reactions to events, we compile an English corpus of written event descriptions. The descriptions depict emotion-eliciting circumstances, and they contain mentions of people who responded emotionally. We annotate all experiencers, including the original author, with the emotions they likely felt. In addition, we link them to the event they found salient (which can be different for different experiencers in a text) by annotating event properties, or appraisals (e.g., the perceived event undesirability, the uncertainty of its outcome). Our analysis reveals patterns in the co-occurrence of people’s emotions in interaction. Hence, this richly-annotated resource provides useful data to study emotions and event evaluations from the perspective of different roles, and it enables the development of experiencer-specific emotion and appraisal classification systems.},
url = {https://aclanthology.org/2022.lrec-1.146},
internaltype = {conferenceproc},
pdf = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.146.pdf},
archiveprefix = {arXiv},
eprint = {2203.10909}
}
@inproceedings{Kadikis2022,
title = {Embarrassingly Simple Performance Prediction for
Abductive Natural Language Inference},
author = {Kadi{\c{k}}is, Em{\=\i}ls and Srivastav, Vaibhav and
Klinger, Roman},
booktitle = {Proceedings of the 2022 Conference of the North
American Chapter of the Association for
Computational Linguistics: Human Language
Technologies},
month = jul,
year = {2022},
address = {Seattle, United States},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.naacl-main.441},
pages = {6031--6037},
abstract = {The task of natural language inference (NLI), to
decide if a hypothesis entails or contradicts a
premise, received considerable attention in recent
years. All competitive systems build on top of
contextualized representations and make use of
transformer architectures for learning an NLI
model. When somebody is faced with a particular NLI
task, they need to select the best model that is
available. This is a time-consuming and
resource-intense endeavour. To solve this practical
problem, we propose a simple method for predicting
the performance without actually fine-tuning the
model. We do this by testing how well the
pre-trained models perform on the aNLI task when
just comparing sentence embeddings with cosine
similarity to what kind of performance is achieved
when training a classifier on top of these
embeddings. We show that the accuracy of the cosine
similarity approach correlates strongly with the
accuracy of the classification approach with a
Pearson correlation coefficient of 0.65. Since the
similarity is orders of magnitude faster to compute
on a given dataset (less than a minute vs. hours),
our method can lead to significant time savings in
the process of model selection.},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2202.10408}
}
@inproceedings{Papay2022,
title = {Constraining Linear-chain {CRF}s to Regular
Languages},
author = {Sean Papay and Roman Klinger and Sebastian Pado},
booktitle = {International Conference on Learning
Representations},
year = {2022},
url = {https://openreview.net/forum?id=jbrgwbv8nD},
archiveprefix = {arXiv},
eprint = {2106.07306},
internaltype = {conferenceproc}
}
@inproceedings{Wuehrl2021b,
author = {Amelie W\"uhrl and Roman Klinger},
title = {Claim Detection in Biomedical Twitter Posts as a
Prerequisite for Fact-Checking},
year = {2021},
booktitle = {Proceedings of the BioCreative VII Challenge
Evaluation Workshop},
url = {https://biocreative.bioinformatics.udel.edu/media/store/files/2021/Posters_pos_1_BC7_Wuehrl.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2104.11639}
}
@inproceedings{DoanDang2021,
title = {Emotion Stimulus Detection in {G}erman News
Headlines},
author = {Doan Dang, Bao Minh and Oberl{\"a}nder, Laura and
Klinger, Roman},
booktitle = {Proceedings of the 17th Conference on Natural
Language Processing (KONVENS 2021)},
month = {6--9 } # sep,
year = {2021},
address = {D{\"u}sseldorf, Germany},
publisher = {KONVENS 2021 Organizers},
url = {https://aclanthology.org/2021.konvens-1.7},
pages = {73--85},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2107.12920}
}
@inproceedings{Casel2021,
title = {Emotion Recognition under Consideration of the
Emotion Component Process Model},
author = {Casel, Felix and Heindl, Amelie and Klinger, Roman},
booktitle = {Proceedings of the 17th Conference on Natural
Language Processing (KONVENS 2021)},
month = {6--9 } # sep,
year = {2021},
address = {D{\"u}sseldorf, Germany},
publisher = {KONVENS 2021 Organizers},
url = {https://aclanthology.org/2021.konvens-1.5},
pages = {49--61},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2107.12895}
}
@inproceedings{Hofmann2020b,
title = {Appraisal Theories for Emotion Classification in
Text},
author = {Hofmann, Jan and Troiano, Enrica and Sassenberg, Kai
and Klinger, Roman},
booktitle = {Proceedings of the 28th International Conference on
Computational Linguistics},
month = dec,
year = {2020},
address = {Barcelona, Spain (Online)},
publisher = {International Committee on Computational
Linguistics},
url = {https://www.aclanthology.org/2020.coling-main.11},
doi = {10.18653/v1/2020.coling-main.11},
pages = {125--138},
abstract = {Automatic emotion categorization has been
predominantly formulated as text classification in
which textual units are assigned to an emotion from
a predefined inventory, for instance following the
fundamental emotion classes proposed by Paul Ekman
(fear, joy, anger, disgust, sadness, surprise) or
Robert Plutchik (adding trust, anticipation). This
approach ignores existing psychological theories to
some degree, which provide explanations regarding
the perception of events. For instance, the
description that somebody discovers a snake is
associated with fear, based on the appraisal as
being an unpleasant and non-controllable
situation. This emotion reconstruction is even
possible without having access to explicit reports
of a subjective feeling (for instance expressing
this with the words {``}I am afraid.{''}). Automatic
classification approaches therefore need to learn
properties of events as latent variables (for
instance that the uncertainty and the mental or
physical effort associated with the encounter of a
snake leads to fear). With this paper, we propose to
make such interpretations of events explicit,
following theories of cognitive appraisal of events,
and show their potential for emotion classification
when being encoded in classification models. Our
results show that high quality appraisal dimension
assignments in event descriptions lead to an
improvement in the classification of discrete
emotion categories. We make our corpus of
appraisal-annotated emotion-associated event
descriptions publicly available.},
pdf = {http://www.romanklinger.de/publications/HofmannTroianoSassenbergKlinger.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2003.14155}
}
@inproceedings{Troiano2020,
title = {Lost in Back-Translation: Emotion Preservation in
Neural Machine Translation},
author = {Troiano, Enrica and Klinger, Roman and Pad{\'o},
Sebastian},
booktitle = {Proceedings of the 28th International Conference on
Computational Linguistics},
month = dec,
year = {2020},
address = {Barcelona, Spain (Online)},
publisher = {International Committee on Computational
Linguistics},
url = {https://www.aclanthology.org/2020.coling-main.384},
doi = {10.18653/v1/2020.coling-main.384},
pages = {4340--4354},
url = {http://www.romanklinger.de/publications/TroianoKlingerPado-coling2020.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Oberlaender2020,
title = {Token Sequence Labeling vs. Clause Classification
for {E}nglish Emotion Stimulus Detection},
author = {Oberl{\"a}nder, Laura Ana Maria and Klinger, Roman},
booktitle = {Proceedings of the Ninth Joint Conference on Lexical
and Computational Semantics},
month = dec,
year = {2020},
address = {Barcelona, Spain (Online)},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/2020.starsem-1.7},
pages = {58--70},
url = {http://www.romanklinger.de/publications/OberlaenderKlingerSTARSEM2020.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2010.07557}
}
@inproceedings{Papay2020,
title = {Dissecting Span Identification Tasks with
Performance Prediction},
author = {Papay, Sean and Klinger, Roman and Pad{\'o},
Sebastian},
booktitle = {Proceedings of the 2020 Conference on Empirical
Methods in Natural Language Processing (EMNLP)},
month = nov,
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/2020.emnlp-main.396},
doi = {10.18653/v1/2020.emnlp-main.396},
pages = {4881--4895},
url = {http://www.romanklinger.de/publications/PapayKlingerPado2020.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2010.02587}
}
@incollection{Klinger2020,
author = {Roman Klinger and Evgeny Kim and Sebastian Pad\'o},
title = {Emotion Analysis for Literary Studies},
booktitle = {Reflektierte algorithmische Textanalyse},
year = {2020},
publisher = {De Gruyter},
address = {Berlin, Boston},
doi = {https://doi.org/10.1515/9783110693973-011},
pages = {237 - 268},
url = {https://www.degruyter.com/view/book/9783110693973/10.1515/9783110693973-011.xml},
internaltype = {conferenceproc}
}
@inproceedings{Haider2020,
title = {{PO}-{EMO}: Conceptualization, Annotation, and
Modeling of Aesthetic Emotions in {G}erman and
{E}nglish Poetry},
author = {Haider, Thomas and Eger, Steffen and Kim, Evgeny and
Klinger, Roman and Menninghaus, Winfried},
booktitle = {Proceedings of The 12th Language Resources and
Evaluation Conference},
month = may,
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://www.aclanthology.org/2020.lrec-1.205},
pages = {1652--1663},
language = {English},
pdf = {http://www.romanklinger.de/publications/HaiderEgerKimKlingerMenninghaus2020LREC_PO-EMO.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2003.07723}
}
@inproceedings{Bostan2020,
title = {{G}ood{N}ews{E}veryone: A Corpus of News Headlines
Annotated with Emotions, Semantic Roles, and Reader
Perception},
author = {Bostan, Laura Ana Maria and Kim, Evgeny and Klinger,
Roman},
booktitle = {Proceedings of The 12th Language Resources and
Evaluation Conference},
month = may,
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://www.aclanthology.org/2020.lrec-1.194},
pages = {1554--1566},
language = {English},
isbn = {979-10-95546-34-4},
pdf = {http://www.romanklinger.de/publications/BostanKimKlinger2020LREC.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1912.03184}
}
@inproceedings{Sabbatino2020,
title = {Automatic Section Recognition in Obituaries},
author = {Sabbatino, Valentino and Bostan, Laura Ana Maria and
Klinger, Roman},
booktitle = {Proceedings of The 12th Language Resources and
Evaluation Conference},
month = may,
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
url = {https://www.aclanthology.org/2020.lrec-1.102},
pages = {817--825},
language = {English},
pdf = {http://www.romanklinger.de/publications/valentino2020.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {2002.12699}
}
@inproceedings{Cevher2019,
author = {Deniz Cevher and Sebastian Zepf and Roman Klinger},
title = {Towards Multimodal Emotion Recognition in German
Speech Events in Cars using Transfer Learning},
booktitle = {Proceedings of the 15th Conference on Natural
Language Processing (KONVENS 2019): Long Papers},
year = {2019},
address = {Erlangen, Germany},
publisher = {German Society for Computational Linguistics \&
Language Technology},
pages = {79--90},
pdf = {http://www.romanklinger.de/publications/CevherZepfKlinger2019.pdf},
url = {https://konvens.org/proceedings/2019/papers/KONVENS2019_paper_16.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1909.02764}
}
@inproceedings{Troiano2019,
title = {Crowdsourcing and Validating Event-focused Emotion
Corpora for {G}erman and {E}nglish},
author = {Troiano, Enrica and Pad{\'o}, Sebastian and Klinger,
Roman},
booktitle = {Proceedings of the 57th Annual Meeting of the
Association for Computational Linguistics},
month = jul,
year = {2019},
address = {Florence, Italy},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/P19-1391},
pdf = {http://www.romanklinger.de/publications/TroianoPadoKlingerACL2019.pdf},
pages = {4005-4011},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1905.13618}
}
@inproceedings{Kim2019,
title = {Frowning {F}rodo, Wincing {L}eia, and a Seriously
Great Friendship: Learning to Classify Emotional
Relationships of Fictional Characters},
author = {Kim, Evgeny and Klinger, Roman},
booktitle = {Proceedings of the 2019 Conference of the North
{A}merican Chapter of the Association for
Computational Linguistics: Human Language
Technologies, Volume 1 (Long and Short Papers)},
month = jun,
year = {2019},
address = {Minneapolis, Minnesota},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/N19-1067},
pages = {647-653},
pdf = {http://www.romanklinger.de/publications/KimKlingerNAACL2019.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1903.12453}
}
@inproceedings{McHardy2019,
title = {Adversarial Training for Satire Detection:
Controlling for Confounding Variables},
author = {McHardy, Robert and Adel, Heike and Klinger, Roman},
booktitle = {Proceedings of the 2019 Conference of the North
{A}merican Chapter of the Association for
Computational Linguistics: Human Language
Technologies, Volume 1 (Long and Short Papers)},
month = jun,
year = {2019},
address = {Minneapolis, Minnesota},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/N19-1069},
pdf = {http://www.romanklinger.de/publications/McHardyAdelKlinger-NAACL2019.pdf},
pages = {660-665},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1902.11145}
}
@inproceedings{Adel2018,
author = {Heike Adel and Laura Ana Maria Bostan and Sean Papay
and Sebastian Padó and Roman Klinger},
title = {{DERE}: A Task and Domain-Independent Slot Filling
Framework for Declarative Relation Extraction},
booktitle = {Proceedings of the 2018 Conference on Empirical
Methods in Natural Language Processing: System
Demonstrations},
year = 2018,
address = {Brussels, Belgium},
month = {October, November},
publisher = {Association for Computational Linguistics},
url = {http://aclanthology.org/D18-2008},
internaltype = {conferenceproc}
}
@inproceedings{Strohm2018,
author = {Florian Strohm and Roman Klinger},
title = {An Empirical Analysis of the Role of Amplifiers,
Downtoners, and Negations in Emotion Classification
in Microblogs},
booktitle = {The 5th IEEE International Conference on Data
Science and Advanced Analytics, Special Track on
Sentiment, Emotion, and Credibility of Information
in Social Data},
year = {2018},
series = {DSAA},
address = {Turin, Italy},
month = {October},
organization = {IEEE},
doi = {10.1109/DSAA.2018.00087},
pdf = {http://www.romanklinger.de/publications/StrohmKlinger-DSAA2018.pdf},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1808.10653}
}
@inproceedings{Bostan2018,
author = {Bostan, Laura Ana Maria and Klinger, Roman},
title = {An Analysis of Annotated Corpora for Emotion
Classification in Text},
booktitle = {Proceedings of the 27th International Conference on
Computational Linguistics},
year = {2018},
publisher = {Association for Computational Linguistics},
pages = {2104-2119},
location = {Santa Fe, New Mexico, USA},
url = {http://aclanthology.org/C18-1179},
internaltype = {conferenceproc}
}
@inproceedings{Kim2018,
author = {Kim, Evgeny and Klinger, Roman},
title = {Who Feels What and Why? Annotation of a Literature
Corpus with Semantic Roles of Emotions},
booktitle = {Proceedings of the 27th International Conference on
Computational Linguistics},
year = {2018},
publisher = {Association for Computational Linguistics},
pages = {1345-1359},
location = {Santa Fe, New Mexico, USA},
url = {http://aclanthology.org/C18-1114},
internaltype = {conferenceproc}
}
@inproceedings{Barnes2018a,
author = {Barnes, Jeremy and Klinger, Roman and Schulte im
Walde, Sabine},
title = {Projecting Embeddings for Domain Adaption: Joint
Modeling of Sentiment Analysis in Diverse Domains},
booktitle = {Proceedings of the 27th International Conference on
Computational Linguistics},
year = {2018},
publisher = {Association for Computational Linguistics},
pages = {818-830},
location = {Santa Fe, New Mexico, USA},
url = {http://aclanthology.org/C18-1070},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1806.04381}
}
@inproceedings{Hartung2018,
author = {Hartung, Matthias and ter Horst, Hendrik and Grimm,
Frank and Diekmann, Tim and Klinger, Roman and
Cimiano, Philipp},
title = {SANTO: A Web-based Annotation Tool for
Ontology-driven Slot Filling},
booktitle = {Proceedings of ACL 2018, System Demonstrations},
month = {July},
year = {2018},
address = {Melbourne, Australia},
publisher = {Association for Computational Linguistics},
pages = {68-73},
abstract = {Supervised machine learning algorithms require
training data whose generation for complex relation
extraction tasks tends to be difficult. Being
optimized for relation extraction at sentence level,
many annotation tools lack in facilitating the
annotation of relational structures that are widely
spread across the text. This leads to non-intuitive
and cumbersome visualizations, making the annotation
process unnecessarily time-consuming. We propose
SANTO, an easy-to-use, domain-adaptive annotation
tool specialized for complex slot filling tasks
which may involve problems of cardinality and
referential grounding. The web-based architecture
enables fast and clearly structured annotation for
multiple users in parallel. Relational structures
are formulated as templates following the
conceptualization of an underlying
ontology. Further, import and export procedures of
standard formats enable interoperability with
external sources and tools.},
url = {http://www.aclanthology.org/P18-4012},
internaltype = {conferenceproc}
}
@inproceedings{Barnes2018,
author = {Barnes, Jeremy and Klinger, Roman and Schulte im
Walde, Sabine},
title = {Bilingual Sentiment Embeddings: Joint Projection of
Sentiment Across Languages},
booktitle = {Proceedings of the 56th Annual Meeting of the
Association for Computational Linguistics (Volume 1:
Long Papers)},
month = {July},
year = {2018},
address = {Melbourne, Australia},
publisher = {Association for Computational Linguistics},
pages = {2483-2493},
abstract = {Sentiment analysis in low-resource languages suffers
from a lack of annotated corpora to estimate
high-performing models. Machine translation and
bilingual word embeddings provide some relief
through cross-lingual sentiment approaches. However,
they either require large amounts of parallel data
or do not sufficiently capture sentiment
information. We introduce Bilingual Sentiment
Embeddings (BLSE), which jointly represent sentiment
information in a source and target language. This
model only requires a small bilingual lexicon, a
source-language corpus annotated for sentiment, and
monolingual word embeddings for each language. We
perform experiments on three language combinations
(Spanish, Catalan, Basque) for sentence-level
cross-lingual sentiment classification and find that
our model significantly out- performs
state-of-the-art methods on four out of six
experimental setups, as well as capturing
complementary information to machine
translation. Our analysis of the resulting embedding
space provides evidence that it represents sentiment
information in the resource-poor target language
without any annotated data in that language.},
url = {http://www.aclanthology.org/P18-1231},
internaltype = {conferenceproc},
archiveprefix = {arXiv},
eprint = {1805.09016}
}
@inproceedings{Terhorst2018,
author = {Ter Horst, Hendrik and Matthias Hartung and Roman
Klinger and Nicole Brazda and Hans Werner Müller and
Philipp Cimiano},
title = {Assessing the Impact of Single and Pairwise Slot
Constraints in a Factor Graph Model for
Template-based Information Extraction},
booktitle = {Natural Language Processing and Information Systems:
23rd International Conference on Applications of
Natural Language to Information Systems, NLDB 2018,
Paris, France, June 13-15, 2018, Proceedings},
year = {2018},
publisher = {Springer International Publishing},
address = {Cham},
url = {https://doi.org/10.1007/978-3-319-91947-8_18},
pdf = {http://www.romanklinger.de/publications/terhorst2018.pdf},
note = { ###bp###},
internaltype = {conferenceproc}
}
@inproceedings{Thorne2018,
author = {Camilo Thorne and Roman Klinger},
title = {On the Semantic Similarity of Disease Mentions in
MEDLINE and Twitter},
booktitle = {Natural Language Processing and Information Systems:
23rd International Conference on Applications of
Natural Language to Information Systems, NLDB 2018,
Paris, France, June 13-15, 2018, Proceedings},
year = {2018},
publisher = {Springer International Publishing},
address = {Cham},
url = {https://doi.org/10.1007/978-3-319-91947-8_34},
pdf = {http://www.romanklinger.de/publications/thorne2018.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Hartung2017,
author = {Hartung, Matthias and Klinger, Roman and Schmidtke,
Franziska and Vogel, Lars},
editor = {Frasincar, Flavius and Ittoo, Ashwin and Nguyen, Le
Minh and M{\'e}tais, Elisabeth},
title = {Identifying Right-Wing Extremism in German Twitter
Profiles: A Classification Approach},
booktitle = {Natural Language Processing and Information Systems:
22nd International Conference on Applications of
Natural Language to Information Systems, NLDB 2017,
Li{\`e}ge, Belgium, June 21-23, 2017, Proceedings},
year = {2017},
publisher = {Springer International Publishing},
address = {Cham},
pages = {320-325},
isbn = {978-3-319-59569-6},
doi = {10.1007/978-3-319-59569-6_40},
url = {http://dx.doi.org/10.1007/978-3-319-59569-6_40},
pdf = {http://www.romanklinger.de/publications/hartung2017-NLDB-short.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Saenger2017,
author = {S{\"a}nger, Mario and Leser, Ulf and Klinger, Roman},
editor = {Frasincar, Flavius and Ittoo, Ashwin and Nguyen, Le
Minh and M{\'e}tais, Elisabeth},
title = {Fine-Grained Opinion Mining from Mobile App Reviews
with Word Embedding Features},
booktitle = {Natural Language Processing and Information Systems:
22nd International Conference on Applications of
Natural Language to Information Systems, NLDB 2017,
Li{\`e}ge, Belgium, June 21-23, 2017, Proceedings},
year = {2017},
publisher = {Springer International Publishing},
address = {Cham},
pages = {3-14},
isbn = {978-3-319-59569-6},
doi = {10.1007/978-3-319-59569-6_1},
url = {http://dx.doi.org/10.1007/978-3-319-59569-6_1},
pdf = {http://www.romanklinger.de/publications/saenger2017-nldb.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Klinger2017,
author = {Klinger, Roman},
editor = {Frasincar, Flavius and Ittoo, Ashwin and Nguyen, Le
Minh and M{\'e}tais, Elisabeth},
title = {Does Optical Character Recognition and Caption
Generation Improve Emotion Detection in Microblog
Posts?},
booktitle = {Natural Language Processing and Information Systems:
22nd International Conference on Applications of
Natural Language to Information Systems, NLDB 2017,
Li{\`e}ge, Belgium, June 21-23, 2017, Proceedings},
year = {2017},
publisher = {Springer International Publishing},
address = {Cham},
pages = {313-319},
isbn = {978-3-319-59569-6},
doi = {10.1007/978-3-319-59569-6_39},
url = {http://dx.doi.org/10.1007/978-3-319-59569-6_39},
pdf = {http://www.romanklinger.de/publications/klinger2017-nldb.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Kicherer2017,
author = {Kicherer, Hanna and Dittrich, Marcel and Grebe,
Lukas and Scheible, Christian and Klinger, Roman},
editor = {Frasincar, Flavius and Ittoo, Ashwin and Nguyen, Le
Minh and M{\'e}tais, Elisabeth},
title = {What You Use, Not What You Do: Automatic
Classification of Recipes},
booktitle = {Natural Language Processing and Information Systems:
22nd International Conference on Applications of
Natural Language to Information Systems, NLDB 2017,
Li{\`e}ge, Belgium, June 21-23, 2017, Proceedings},
year = {2017},
publisher = {Springer International Publishing},
address = {Cham},
pages = {197-209},
isbn = {978-3-319-59569-6},
doi = {10.1007/978-3-319-59569-6_22},
url = {http://dx.doi.org/10.1007/978-3-319-59569-6_22},
pdf = {http://www.romanklinger.de/publications/kicherer2017-nldb.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Aisopos2012,
author = {Aisopos, Fotis and Kardara, Magdalini and Senger,
Philipp and Klinger, Roman and Papaoikonomou,
Athanasios and Tserpes, Konstantinos and Gardner,
Michael and Varvarigou, Theodora A.},
title = {E-Government and Policy Simulation in Intelligent
Virtual Environments.},
booktitle = {WEBIST},
year = {2012},
editor = {Krempels, Karl-Heinz and Cordeiro, José},
pages = {129-135},
publisher = {SciTePress},
pdf = {http://www.romanklinger.de/publications/+Spaces_WEBIST.pdf},
url = {http://dblp.uni-trier.de/db/conf/webist/webist2012.html#AisoposKSKPTGV12},
internaltype = {conferenceproc}
}
@inproceedings{Gurulingappa2010,
author = {Harsha Gurulingappa and Roman Klinger and Martin
Hofmann-Apitius and Juliane Fluck },
title = {An Empirical Evaluation of Resources for the
Identification of Diseases and Adverse Effects in
Biomedical Literature},
booktitle = {{2nd Workshop on Building and evaluating resources
for biomedical text mining (7th edition of the
Language Resources and Evaluation Conference)}},
year = {2010},
address = {Valetta, Malta},
month = {May},
url = {http://www.nactem.ac.uk/biotxtm/papers/Gurulingappa.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Gurulingappa2009,
author = {Harsha Gurulingappa and Bernd M\"uller and Roman
Klinger and Heinz-Theo Mevissen and Martin
Hofmann-Apitius and Juliane Fluck and Christoph
M. Friedrich},
title = {Patent Retrieval in Chemistry based on semantically
tagged Named Entities},
booktitle = {The Eighteenth Text RETrieval Conference (TREC 2009)
Proceedings},
year = {2009},
editor = {Ellen M. Voorhees and Lori P. Buckland},
address = {Gaithersburg, Maryland, USA},
month = {November},
url = {http://trec.nist.gov/pubs/trec18/papers/scai.CHEM.pdf},
internaltype = {conferenceproc}
}
@inproceedings{klinger:2011:RANLP,
author = {Klinger, Roman},
title = {Automatically Selected Skip Edges in Conditional
Random Fields for Named Entity Recognition},
booktitle = {Proceedings of the International Conference Recent
Advances in Natural Language Processing 2011},
year = {2011},
pages = {580-585},
address = {Hissar, Bulgaria},
month = {September},
publisher = {RANLP 2011 Organising Committee},
url = {http://aclanthology.org/R11-1082},
internaltype = {conferenceproc}
}
@inproceedings{Klinger2014a,
author = {Roman Klinger and Philipp Cimiano},
title = {The {USAGE} review corpus for fine grained multi lingual opinion
analysis},
booktitle = {Proceedings of the Ninth International Conference on Language Resources
and Evaluation (LREC'14)},
year = {2014},
editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn
Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno
and Jan Odijk and Stelios Piperidis},
pages = {2211-2218},
address = {Reykjavik, Iceland},
month = {May},
publisher = {European Language Resources Association (ELRA)},
note = {ACL Anthology Identifier: L14-1656},
date = {26-31},
isbn = {978-2-9517408-8-4},
language = {english},
pdf = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/85_Paper.pdf},
url = {https://www.aclanthology.org/L14-1656/},
internaltype = {conferenceproc}
}
@inproceedings{klinger2015,
author = {Klinger, Roman and Cimiano, Philipp},
title = {Instance Selection Improves Cross-Lingual Model Training for Fine-Grained
Sentiment Analysis},
booktitle = {Proceedings of the Nineteenth Conference on Computational Natural
Language Learning},
year = {2015},
pages = {153-163},
address = {Beijing, China},
month = {July},
publisher = {Association for Computational Linguistics},
url = {http://www.aclanthology.org/K15-1016},
internaltype = {conferenceproc}
}
@inproceedings{klinger-cimiano:2013:Short,
author = {Klinger, Roman and Cimiano, Philipp},
title = {Bi-directional Inter-dependencies of Subjective
Expressions and Targets and their Value for a Joint
Model},
booktitle = {Proceedings of the 51st Annual Meeting of the
Association for Computational Linguistics (Volume 2:
Short Papers)},
year = {2013},
pages = {848-854},
address = {Sofia, Bulgaria},
month = {August},
publisher = {Association for Computational Linguistics},
url = {http://www.aclanthology.org/P13-2147},
internaltype = {conferenceproc}
}
@inproceedings{Klinger2009,
author = {Roman Klinger and Christoph M. Friedrich},
title = {User's Choice of Precision and Recall in Named
Entity Recognition},
booktitle = {Proceedings of Recent Advances in Natural Language
Processing (RANLP)},
year = {2009},
editor = {Galia Angelova and Kalina Bontcheva and Ruslan
Mitkov and Nicolas Nicolov and Nicolai Nikolov},
pages = {192-196},
address = {Borovets, Bulgaria},
month = {September},
pdf = {https://www.aclanthology.org/R09-1036/},
owner = {rklinger},
timestamp = {2009.06.04},
internaltype = {conferenceproc}
}
@inproceedings{Klinger2009a,
author = {Roman Klinger and Christoph M. Friedrich},
title = {Feature Subset Selection in Conditional Random
Fields for Named Entity Recognition},
booktitle = {Proceedings of Recent Advances in Natural Language
Processing (RANLP)},
year = {2009},
editor = {Galia Angelova and Kalina Bontcheva and Ruslan
Mitkov and Nicolas Nicolov and Nicolai Nikolov},
pages = {185-191},
address = {Borovets, Bulgaria},
month = {September},
pdf = {https://www.aclanthology.org/R09-1035/},
internaltype = {conferenceproc},
note = {###nombp###}
}
@inproceedings{Klinger2007a,
author = {Roman Klinger and Christoph M. Friedrich and Juliane
Fluck and Martin Hofmann-Apitius},
title = {{Named Entity Recognition with Combinations of
Conditional Random Fields}},
booktitle = {Proceedings of the Second BioCreative Challenge
Evaluation Workshop},
year = {2007},
pages = {89-91},
address = {Madrid, Spain},
month = {April},
pdf = {http://www.romanklinger.de/publications/bc2.pdf},
internaltype = {conferenceproc}
}
@inproceedings{Klinger2006a,
author = {Roman Klinger and G\"unter Rudolph},
title = {{Evolutionary Composition of Music with Learned
Melody Evaluation}},
booktitle = {Conference on COMPUTATIONAL INTELLIGENCE,
MAN-MACHINE SYSTEMS and CYBERNETICS (CIMMACS '06)},
year = {2006},
editor = {Nikos Mastorakis and Antonella Cecchi},
pages = {234-239},
address = {Venice, Italy},
month = {November},
internaltype = {conferenceproc},
note = {###bsp###}
}
@inproceedings{Klinger2012,
author = {Klinger, Roman and Senger, Philipp and Madan, Sumit
and Jacovi, Michal},
title = {Online Communities Support Policy-Making: The Need
for Data Analysis},
booktitle = {Electronic Participation},
year = {2012},
editor = {Tambouris, Efthimios and Macintosh, Ann and Sæbø,
Øystein},
volume = {7444},
series = {Lecture Notes in Computer Science},
pages = {132-143},
publisher = {Springer Berlin Heidelberg},
doi = {10.1007/978-3-642-33250-0_12},
isbn = {978-3-642-33249-4},
url = {http://dx.doi.org/10.1007/978-3-642-33250-0_12},
internaltype = {conferenceproc}
}
@inproceedings{Ling2016,
author = {Ling, Jennifer and Klinger, Roman},
editor = {Sack, Harald and Rizzo, Giuseppe and Steinmetz,
Nadine and Mladeni{\'{c}}, Dunja and Auer, S{\"o}ren
and Lange, Christoph},
title = {An Empirical, Quantitative Analysis of the
Differences Between Sarcasm and Irony},
booktitle = {The Semantic Web: ESWC 2016 Satellite Events,
Heraklion, Crete, Greece, May 29 -- June 2, 2016,
Revised Selected Papers},
year = {2016},
publisher = {Springer International Publishing},
pages = {203-216},
isbn = {978-3-319-47602-5},
doi = {10.1007/978-3-319-47602-5_39},
url = {http://dx.doi.org/10.1007/978-3-319-47602-5_39},
pdf = {http://www.romanklinger.de/publications/ling2016.pdf},
internaltype = {conferenceproc},
note = { ###bp###}
}
@inproceedings{Mueller2010,
author = {Bernd M\"uller and Roman Klinger and Harsha
Gurulingappa and Heinz-Theodor Mevissen and Martin
Hofmann-Apitius and Juliane Fluck and Christoph
M. Friedrich},
title = {Abstracts versus Full Texts and Patents: A
Quantitative Analysis of Biomedical Entities},
booktitle = {Proceedings of the 1st IRF Conference},
year = {2010},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
url = {http://link.springer.com/chapter/10.1007/978-3-642-13084-7_12},
internaltype = {conferenceproc}
}
@inproceedings{mccrae-cimiano-klinger:2013:EMNLP,
author = {McCrae, John Philip and Cimiano, Philipp and
Klinger, Roman},
title = {Orthonormal Explicit Topic Analysis for
Cross-Lingual Document Matching},
booktitle = {Proceedings of the 2013 Conference on Empirical
Methods in Natural Language Processing},
year = {2013},
pages = {1732-1740},
address = {Seattle, Washington, USA},
month = {October},
publisher = {Association for Computational Linguistics},
url = {http://www.aclanthology.org/D13-1179},
internaltype = {conferenceproc}
}
@inproceedings{Saenger2016,
author = {Mario Sänger and Ulf Leser and Steffen Kemmerer and
Peter Adolphs and Roman Klinger},
title = {{SCARE ― The Sentiment Corpus of App Reviews with
Fine-grained Annotations in German}},
booktitle = {Proceedings of the Tenth International Conference on
Language Resources and Evaluation (LREC 2016)},
year = {2016},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid
Choukri and Thierry Declerck and Marko Grobelnik and
Bente Maegaard and Joseph Mariani and Asuncion
Moreno and Jan Odijk and Stelios Piperidis},
address = {Paris, France},
month = {may},
publisher = {European Language Resources Association (ELRA)},
date = {23-28},
pdf = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/59_Paper.pdf},
isbn = {978-2-9517408-9-1},
language = {english},
location = {Portorož, Slovenia},
url = {http://www.lrec-conf.org/proceedings/lrec2016/summaries/59.html},
url = {https://www.aclanthology.org/L16-1178/},
internaltype = {conferenceproc}
}
@inproceedings{Scheible2016,
author = {Scheible, Christian and Klinger, Roman and Pad\'{o},
Sebastian},
title = {Model Architectures for Quotation Detection},
booktitle = {Proceedings of the 54th Annual Meeting of the
Association for Computational Linguistics (Volume 1:
Long Papers)},
month = {August},
year = {2016},
address = {Berlin, Germany},
publisher = {Association for Computational Linguistics},
pages = {1736-1745},
url = {https://www.aclanthology.org/P16-1164/},
internaltype = {conferenceproc}
}