klinger.bib
@inproceedings{Greschner2025b,
author = {Lynn Greschner and Amelie W\"uhrl and Roman Klinger},
title = {{QoLAS:} A {Reddit} Corpus of Health-Related Quality
of Life Aspects of Mental Disorders},
booktitle = {Proceedings of the Workshop on Biomedical Language
Processing},
year = {2025},
address = {Vienna, Austria},
organization = {Association for Computational Linguistics},
note = {accepted},
internaltype = {workshop}
}
@inproceedings{Papay2025,
title = {Regular-pattern-sensitive CRFs for Distant Label
Interactions},
author = {Sean Papay and Roman Klinger and Sebastian Pado},
year = {2025},
eprint = {2411.12484},
archiveprefix = {arXiv},
primaryclass = {cs.LG},
url = {https://arxiv.org/abs/2411.12484},
booktitle = {Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM)},
address = {Vienna, Austria},
organization = {Association for Computational Linguistics},
note = {accepted},
internaltype = {workshop}
}
@inproceedings{HofmannSindermannKlinger2024,
title = {Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering},
author = {Jan Hofmann and Cornelia Sindermann and Roman Klinger},
eprint = {2409.04122},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {http://arxiv.org/abs/2409.04122},
internaltype = {workshop},
booktitle = {Proceedings of the First Workshop for Research on Agent Language Models (REALM)},
year = {2025},
address = {Vienna, Austria},
organization = {Association for Computational Linguistics},
note = {accepted}
}
@inproceedings{Wuehrl2024,
title = {{IMS}{\_}medic{ALY} at {\#}{SMM}4{H} 2024: Detecting
Impacts of Outdoor Spaces on Social Anxiety with
Data Augmented Ensembling},
author = {Wuehrl, Amelie and Greschner, Lynn and Menchaca
Resendiz, Yarik and Klinger, Roman},
editor = {Xu, Dongfang and Gonzalez-Hernandez, Graciela},
booktitle = {Proceedings of The 9th Social Media Mining for
Health Research and Applications (SMM4H 2024)
Workshop and Shared Tasks},
month = aug,
year = {2024},
address = {Bangkok, Thailand},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.smm4h-1.19},
pages = {83--87},
abstract = {Many individuals affected by Social Anxiety Disorder
turn to social media platforms to share their
experiences and seek advice. This includes
discussing the potential benefits of engaging with
outdoor environments. As part of {\#}SMM4H 2024,
Shared Task 3 focuses on classifying the effects of
outdoor spaces on social anxiety symptoms in Reddit
posts. In our contribution to the task, we explore
the effectiveness of domain-specific models (trained
on social media data {--} SocBERT) against general
domain models (trained on diverse datasets {--}
BERT, RoBERTa, GPT-3.5) in predicting the sentiment
related to outdoor spaces. Further, we assess the
benefits of augmenting sparse human-labeled data
with synthetic training instances and evaluate the
complementary strengths of domain-specific and
general classifiers using an ensemble model. Our
results show that (1) fine-tuning small,
domain-specific models generally outperforms large
general language models in most cases. Only one
large language model (GPT-4) exhibits performance
comparable to the fine-tuned models (52{\%}
F1). Further, we find that (2) synthetic data does
improve the performance of fine-tuned models in some
cases, and (3) models do not appear to complement
each other in our ensemble setup.},
internaltype = {workshop}
}
@inproceedings{Schaefer2024,
title = {Hierarchical Adversarial Correction to Mitigate
Identity Term Bias in Toxicity Detection},
author = {Sch{\"a}fer, Johannes and Heid, Ulrich and Klinger,
Roman},
editor = {De Clercq, Orph{\'e}e and Barriere, Valentin and
Barnes, Jeremy and Klinger, Roman and Sedoc,
Jo{\~a}o and Tafreshi, Shabnam},
booktitle = {Proceedings of the 14th Workshop on Computational
Approaches to Subjectivity, Sentiment, {\&} Social
Media Analysis},
month = aug,
year = {2024},
address = {Bangkok, Thailand},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.wassa-1.4},
pdf = {https://www.romanklinger.de/publications/SchaeferHeidKlingerWASSA2024.pdf},
pages = {35--51},
abstract = {Corpora that are the fundament for toxicity
detection contain such expressions typically
directed against a target individual or group, e.g.,
people of a specific gender or ethnicity. Prior work
has shown that the target identity mention can
constitute a confounding variable. As an example, a
model might learn that Christians are always
mentioned in the context of hate speech. This
misguided focus can lead to a limited generalization
to newly emerging targets that are not found in the
training data. In this paper, we hypothesize and
subsequently show that this issue can be mitigated
by considering targets on different levels of
specificity. We distinguish levels of (1) the
existence of a target, (2) a class (e.g., that the
target is a religious group), or (3) a specific
target group (e.g., Christians or Muslims). We
define a target label hierarchy based on these three
levels and then exploit this hierarchy in an
adversarial correction for the lowest level
(i.e. (3)) while maintaining some basic target
features. This approach does not lower the toxicity
detection performance but increases the
generalization to targets not being available at
training time.},
internaltype = {workshop}
}
@inproceedings{Ronningstad2024,
title = {Entity-Level Sentiment: More than the Sum of Its
Parts},
author = {R{\o}nningstad, Egil and Klinger, Roman and Velldal,
Erik and {\O}vrelid, Lilja},
editor = {De Clercq, Orph{\'e}e and Barriere, Valentin and
Barnes, Jeremy and Klinger, Roman and Sedoc,
Jo{\~a}o and Tafreshi, Shabnam},
booktitle = {Proceedings of the 14th Workshop on Computational
Approaches to Subjectivity, Sentiment, {\&} Social
Media Analysis},
month = aug,
year = {2024},
address = {Bangkok, Thailand},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.wassa-1.8},
pages = {84--96},
abstract = {In sentiment analysis of longer texts, there may be
a variety of topics discussed, of entities
mentioned, and of sentiments expressed regarding
each entity. We find a lack of studies exploring how
such texts express their sentiment towards each
entity of interest, and how these sentiments can be
modelled. In order to better understand how
sentiment regarding persons and organizations (each
entity in our scope) is expressed in longer texts,
we have collected a dataset of expert annotations
where the overall sentiment regarding each entity is
identified, together with the sentence-level
sentiment for these entities separately. We show
that the reader{'}s perceived sentiment regarding an
entity often differs from an arithmetic aggregation
of sentiments at the sentence level. Only 70{\%} of
the positive and 55{\%} of the negative entities
receive a correct overall sentiment label when we
aggregate the (human-annotated) sentiment labels for
the sentences where the entity is mentioned. Our
dataset reveals the complexity of entity-specific
sentiment in longer texts, and allows for more
precise modelling and evaluation of such sentiment
expressions.},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2407.03916},
pdf = {https://www.romanklinger.de/publications/RønningstadKlingerVelldalØvrelid_WASSA2024.pdf}
}
@inproceedings{Barreiss20242,
author = {Barei\ss{}, Patrick and Klinger, Roman and Barnes,
Jeremy},
title = {English Prompts are Better for {NLI}-based Zero-Shot
Emotion Classification than Target-Language Prompts},
year = {2024},
isbn = {9798400701726},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589335.3651902},
doi = {10.1145/3589335.3651902},
abstract = {Emotion classification in text is a challenging task
due to the processes involved when interpreting a
textual description of a potential emotion
stimulus. In addition, the set of emotion categories
is highly domain-specific. For instance, literature
analysis might require the use of aesthetic emotions
(e.g., finding something beautiful), and social
media analysis could benefit from fine-grained sets
(e.g., separating anger from annoyance) than only
those that represent basic categories as they have
been proposed by Paul Ekman (anger, disgust, fear,
joy, surprise, sadness). This renders the task an
interesting field for zero-shot classifications, in
which the label set is not known at model
development time. Unfortunately, most resources for
emotion analysis are English, and therefore, most
studies on emotion analysis have been performed in
English, including those that involve prompting
language models for text labels. This leaves us with
a research gap that we address in this paper: In
which language should we prompt for emotion labels
on non-English texts? This is particularly of
interest when we have access to a multilingual large
language model, because we could request labels with
English prompts even for non-English data. Our
experiments with natural language inference-based
language models show that it is consistently better
to use English prompts even if the data is in a
different language.},
booktitle = {Companion Proceedings of the ACM on Web Conference
2024},
pages = {1318–1326},
numpages = {9},
location = {Singapore, Singapore},
series = {WWW '24},
internaltype = {workshop}
}
@inproceedings{wegge-klinger-2024-topic,
title = {Topic Bias in Emotion Classification},
author = {Wegge, Maximilian and Klinger, Roman},
editor = {van der Goot, Rob and Bak, JinYeong and
M{\"u}ller-Eberstein, Max and Xu, Wei and Ritter,
Alan and Baldwin, Tim},
booktitle = {Proceedings of the Ninth Workshop on Noisy and
User-generated Text (W-NUT 2024)},
month = mar,
year = {2024},
address = {San {\.G}iljan, Malta},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2024.wnut-1.9},
pages = {89--103},
abstract = {Emotion corpora are typically sampled based on
keyword/hashtag search or by asking study
participants to generate textual instances. In any
case, these corpora are not uniform samples
representing the entirety of a domain. We
hypothesize that this practice of data acquision
leads to unrealistic correlations between
overrepresented topics in these corpora that harm
the generalizability of models. Such topic bias
could lead to wrong predictions for instances like
{``}I organized the service for my aunt{'}s
funeral.{''} when funeral events are overpresented
for instances labeled with sadness, despite the
emotion of pride being more appropriate here. In
this paper, we study this topic bias both from the
data and the modeling perspective. We first label a
set of emotion corpora automatically via topic
modeling and show that emotions in fact correlate
with specific topics. Further, we see that emotion
classifiers are confounded by such topics. Finally,
we show that the established debiasing method of
adversarial correction via gradient reversal
mitigates the issue. Our work points out issues with
existing emotion corpora and that more
representative resources are required for fair
evaluation of models predicting affective concepts
from text.},
internaltype = {workshop},
pdf = {https://www.romanklinger.de/publications/WeggeKlinger2024.pdf},
eprint = {2312.09043},
archiveprefix = {arXiv},
primaryclass = {cs.CL}
}
@inproceedings{Klinger2023a,
author = {Roman Klinger},
title = {Where are We in Event-centric Emotion Analysis?
Bridging Emotion Role Labeling and Appraisal-based
Approaches},
booktitle = {Proceedings of the Big Picture Workshop: Crafting a
Research Narrative},
year = {2023},
month = {December},
address = {Singapore},
organization = {EMNLP},
publisher = {Association for Computational Linguistics},
url = {https://www.romanklinger.de/publications/klinger2023.pdf},
archiveprefix = {arXiv},
eprint = {2309.02092},
primaryclass = {cs.CL},
internaltype = {workshop}
}
@inproceedings{MenchacaResendiz2023b,
title = {Emotion-Conditioned Text Generation through
Automatic Prompt Optimization},
author = {Menchaca Resendiz, Yarik and Klinger, Roman},
booktitle = {Proceedings of the 1st Workshop on Taming Large
Language Models: Controllability in the era of
Interactive Assistants!},
month = sep,
year = 2023,
address = {Prague, Czech Republic},
publisher = {Association for Computational Linguistics},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2308.04857},
pdf = {https://www.romanklinger.de/publications/MenchacaResendiz_Klinger_TLLM2023.pdf}
}
@inproceedings{Wegge2023,
author = {Maximilian Wegge and Roman Klinger},
title = {Automatic Emotion Experiencer Recognition},
booktitle = {3rd Workshop on Computational Linguistics for the
Political and Social Sciences (CPSS)},
year = 2023,
month = may,
pdf = {https://www.romanklinger.de/publications/WeggeKlinger2023.pdf},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2305.16731}
}
@inproceedings{Velutharambath2023,
title = {{UNIDECOR}: A Unified Deception Corpus for
Cross-Corpus Deception Detection},
author = {Velutharambath, Aswathy and Klinger, Roman},
booktitle = {Proceedings of the 13th Workshop on Computational
Approaches to Subjectivity, Sentiment, {\&} Social
Media Analysis},
month = jul,
year = {2023},
address = {Toronto, Canada},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2023.wassa-1.5},
pages = {39--51},
abstract = {Verbal deception has been studied in psychology,
forensics, and computational linguistics for a
variety of reasons, like understanding behaviour
patterns, identifying false testimonies, and
detecting deception in online communication. Varying
motivations across research fields lead to
differences in the domain choices to study and in
the conceptualization of deception, making it hard
to compare models and build robust deception
detection systems for a given language. With this
paper, we improve this situation by surveying
available English deception datasets which include
domains like social media reviews, court
testimonials, opinion statements on specific topics,
and deceptive dialogues from online strategy
games. We consolidate these datasets into a single
unified corpus. Based on this resource, we conduct a
correlation analysis of linguistic cues of deception
across datasets to understand the differences and
perform cross-corpus modeling experiments which show
that a cross-domain generalization is challenging to
achieve. The unified deception corpus (UNIDECOR) can
be obtained from
https://www.ims.uni-stuttgart.de/data/unidecor.},
internaltype = {workshop},
pdf = {https://www.romanklinger.de/publications/VelutharambathKlinger_UNIDECOR_WASSA2023.pdf},
archiveprefix = {arXiv},
eprint = {2306.02827}
}
@inproceedings{Wuehrl2023,
author = {Amelie W\"uhrl and Lara Grimminger and Roman
Klinger},
title = {An Entity-based Claim Extraction Pipeline for
Real-world Fact-checking},
month = {May},
year = {2023},
booktitle = {Proceedings of the Sixth Fact Extraction and
VERification Workshop (FEVER)},
address = {Dubrovnik, Croatia},
organization = {Association for Computational Linguistics},
pdf = {https://www.romanklinger.de/publications/WuehrlKlingerFEVER2023.pdf},
url = {https://aclanthology.org/2023.fever-1.3},
pages = {29–37},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2304.05268}
}
@inproceedings{Wegge2022,
title = {Experiencer-Specific Emotion and Appraisal
Prediction},
author = {Wegge, Maximilian and Troiano, Enrica and
Oberlaender, Laura Ana Maria and Klinger, Roman},
booktitle = {Proceedings of the Fifth Workshop on Natural
Language Processing and Computational Social Science
(NLP+CSS)},
month = nov,
year = {2022},
address = {Abu Dhabi, UAE},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.nlpcss-1.3},
pages = {25--32},
abstract = {Emotion classification in NLP assigns emotions to
texts, such as sentences or paragraphs. With texts
like {``}I felt guilty when he cried{''}, focusing
on the sentence level disregards the standpoint of
each participant in the situation: the writer
({``}I{''}) and the other entity ({``}he{''}) could
in fact have different affective states. The
emotions of different entities have been considered
only partially in emotion semantic role labeling, a
task that relates semantic roles to emotion cue
words. Proposing a related task, we narrow the focus
on the experiencers of events, and assign an emotion
(if any holds) to each of them. To this end, we
represent each emotion both categorically and with
appraisal variables, as a psychological access to
explaining why a person develops a particular
emotion. On an event description corpus, our
experiencer-aware models of emotions and appraisals
outperform the experiencer-agnostic baselines,
showing that disregarding event participants is an
oversimplification for the emotion detection task.},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2210.12078}
}
@inproceedings{Wuehrl2022,
title = {Entity-based Claim Representation Improves
Fact-Checking of Medical Content in Tweets},
author = {W{\"u}hrl, Amelie and Klinger, Roman},
booktitle = {Proceedings of the 9th Workshop on Argument Mining},
month = oct,
year = {2022},
address = {Online and in Gyeongju, Republic of Korea},
publisher = {International Conference on Computational
Linguistics},
url = {https://aclanthology.org/2022.argmining-1.18},
pdf = {https://www.romanklinger.de/publications/WuehrlKlinger_Argmining2022.pdf},
archiveprefix = {arXiv},
eprint = {2209.07834},
pages = {187--198},
abstract = {False medical information on social media poses harm
to people{'}s health. While the need for biomedical
fact-checking has been recognized in recent years,
user-generated medical content has received
comparably little attention. At the same time,
models for other text genres might not be reusable,
because the claims they have been trained with are
substantially different. For instance, claims in the
SciFact dataset are short and focused: {``}Side
effects associated with antidepressants increases
risk of stroke{''}. In contrast, social media holds
naturally-occurring claims, often embedded in
additional context: ''{`}If you take antidepressants
like SSRIs, you could be at risk of a condition
called serotonin syndrome{'} Serotonin syndrome
nearly killed me in 2010. Had symptoms of stroke and
seizure.{''} This showcases the mismatch between
real-world medical claims and the input that
existing fact-checking systems expect. To make
user-generated content checkable by existing models,
we propose to reformulate the social-media input in
such a way that the resulting claim mimics the claim
characteristics in established datasets. To
accomplish this, our method condenses the claim with
the help of relational entity information and either
compiles the claim out of an entity-relation-entity
triple or extracts the shortest phrase that contains
these elements. We show that the reformulated input
improves the performance of various fact-checking
models as opposed to checking the tweet text in its
entirety.},
internaltype = {workshop},
note = {###run###}
}
@inproceedings{Sabbatino2022,
title = {{``}splink{''} is happy and {``}phrouth{''} is
scary: Emotion Intensity Analysis for Nonsense
Words},
author = {Sabbatino, Valentino and Troiano, Enrica and
Schweitzer, Antje and Klinger, Roman},
booktitle = {Proceedings of the 12th Workshop on Computational
Approaches to Subjectivity, Sentiment {\&} Social
Media Analysis},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.wassa-1.4},
pages = {37--50},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2202.12132}
}
@inproceedings{Kreuter2022,
title = {Items from Psychometric Tests as Training Data for
Personality Profiling Models of {T}witter Users},
author = {Kreuter, Anne and Sassenberg, Kai and Klinger,
Roman},
booktitle = {Proceedings of the 12th Workshop on Computational
Approaches to Subjectivity, Sentiment {\&} Social
Media Analysis},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.wassa-1.35},
pages = {315--323},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2202.10415}
}
@inproceedings{Khlyzova2022,
title = {On the Complementarity of Images and Text for the
Expression of Emotions in Social Media},
author = {Khlyzova, Anna and Silberer, Carina and Klinger,
Roman},
booktitle = {Proceedings of the 12th Workshop on Computational
Approaches to Subjectivity, Sentiment {\&} Social
Media Analysis},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.wassa-1.1},
pages = {1--15},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2202.07427}
}
@inproceedings{Plazadelarco2021,
author = {Flor M. {Plaza-del-Arco} and Sercan Halat and
Sebastian Padó and Roman Klinger},
title = {Multi-Task Learning with Sentiment, Emotion, and
Target Detection to Recognize Hate Speech and
Offensive Language},
url = {http://ceur-ws.org/Vol-3159/T1-30.pdf},
year = 2021,
pages = {297--318},
booktitle = {FIRE 2021 Working Notes},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2109.10255}
}
@inproceedings{Wuehrl2021,
title = {Claim Detection in Biomedical {T}witter Posts},
author = {W{\"u}hrl, Amelie and Klinger, Roman},
booktitle = {Proceedings of the 20th Workshop on Biomedical
Language Processing},
month = jun,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.info/2021.bionlp-1.15},
pages = {131--142},
pdf = {https://www.romanklinger.de/publications/WuehrlKlinger_BioNLP2021.pdf},
internaltype = {workshop}
}
@inproceedings{Oberlaender2020b,
title = {Experiencers, Stimuli, or Targets: Which Semantic
Roles Enable Machine Learning to Infer the
Emotions?},
author = {Laura Oberl\"ander and Kevin Reich and Roman
Klinger},
booktitle = {Proceedings of the Third Workshop on Computational
Modeling of People{'}s Opinions, Personality, and
Emotions in Social Media},
month = dec,
year = {2020},
address = {Barcelona, Spain},
publisher = {Association for Computational Linguistics},
pdf = {http://www.romanklinger.de/publications/OberlaenderReichKlinger2020peoples.pdf},
url = {https://www.aclanthology.org/2020.peoples-1.12/},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2011.01599}
}
@inproceedings{Armbrust2020,
title = {A Computational Analysis of Financial and
Environmental Narratives within Financial Reports
and its Value for Investors},
author = {Armbrust, Felix and Sch{\"a}fer, Henry and Klinger,
Roman},
booktitle = {Proceedings of the 1st Joint Workshop on Financial
Narrative Processing and MultiLing Financial
Summarisation},
month = dec,
year = {2020},
address = {Barcelona, Spain (Online)},
publisher = {COLING},
url = {https://www.aclanthology.org/2020.fnp-1.31},
pages = {181--194},
pdf = {http://www.romanklinger.de/publications/ArmbrustSchaeferKlinger2020.pdf},
internaltype = {workshop}
}
@inproceedings{Troiano2021,
title = {Emotion Ratings: How Intensity, Annotation
Confidence and Agreements are Entangled},
author = {Troiano, Enrica and Pad{\'o}, Sebastian and Klinger,
Roman},
booktitle = {Proceedings of the Eleventh Workshop on
Computational Approaches to Subjectivity, Sentiment
and Social Media Analysis},
month = apr,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/2021.wassa-1.5},
pages = {40--49},
pdf = {http://www.romanklinger.de/publications/TroianoPadoKlingerWASSA2021.pdf},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2103.01667}
}
@inproceedings{Grimminger2021,
title = {Hate Towards the Political Opponent: A {T}witter
Corpus Study of the 2020 {US} Elections on the Basis
of Offensive Speech and Stance Detection},
author = {Grimminger, Lara and Klinger, Roman},
booktitle = {Proceedings of the Eleventh Workshop on
Computational Approaches to Subjectivity, Sentiment
and Social Media Analysis},
month = apr,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/2021.wassa-1.18},
pages = {171--180},
pdf = {http://www.romanklinger.de/publications/GrimmingerKlingerWASSA2021.pdf},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2103.01664}
}
@inproceedings{Hofmann2021,
title = {Emotion-Aware, Emotion-Agnostic, or Automatic:
Corpus Creation Strategies to Obtain Cognitive Event
Appraisal Annotations},
author = {Hofmann, Jan and Troiano, Enrica and Klinger, Roman},
booktitle = {Proceedings of the Eleventh Workshop on
Computational Approaches to Subjectivity, Sentiment
and Social Media Analysis},
month = apr,
year = {2021},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/2021.wassa-1.17},
pages = {160--170},
pdf = {http://www.romanklinger.de/publications/HofmannTroianoKlingerWASSA2021.pdf},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2102.12858}
}
@inproceedings{Helbig2020,
title = {Challenges in Emotion Style Transfer: An Exploration
with a Lexical Substitution Pipeline},
author = {Helbig, David and Troiano, Enrica and Klinger,
Roman},
booktitle = {Proceedings of the Eighth International Workshop on
Natural Language Processing for Social Media},
month = jul,
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/2020.socialnlp-1.6},
pdf = {http://www.romanklinger.de/publications/HelbigTroianoKlingerSocialNLP2020.pdf},
pages = {41--50},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {2005.07617}
}
@inproceedings{Kim2019a,
title = {An Analysis of Emotion Communication Channels in
Fan-Fiction: Towards Emotional Storytelling},
author = {Kim, Evgeny and Klinger, Roman},
booktitle = {Proceedings of the Second Workshop on Storytelling},
year = {2019},
address = {Florence, Italy},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/W19-3406},
pdf = {http://www.romanklinger.de/publications/KimKlingerStoryNLP2019ACL.pdf},
pages = {56-64},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {1906.02402}
}
@inproceedings{Bostan2019,
author = {Bostan, Laura Ana Maria and Klinger, Roman},
title = {Exploring fine-tuned embeddings that model
intensifiers for emotion analysis},
booktitle = {Proceedings of the Tenth Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
month = jun,
year = {2019},
address = {Minneapolis, USA},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/W19-1304},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {1904.03164}
}
@inproceedings{Klinger2018x,
author = {Roman Klinger and Orph\'ee de Clercq and Saif
M. Mohammad and Alexandra Balahur},
title = {{IEST}: {WASSA}-2018 Implicit Emotions Shared Task},
booktitle = {Proceedings of the 9th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = {2018},
address = {Brussels, Belgium},
month = {November},
organization = {Association for Computational Linguistics},
pdf = {http://implicitemotions.wassa2018.com/paper/iest-description-2018.pdf},
url = {https://www.aclanthology.org/W18-6206},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {1809.01083}
}
@inproceedings{Brazda2017,
author = {Nicole Brazda and Hendrik ter Horst and Matthias
Hartung and Cord Wiljes and Veronica Estrada and
Roman Klinger and Wolfgang Kuchinke and Hans Werner
M\"uller and Philipp Cimiano},
title = {{SCIO}: An Ontology to Support the Formalization of
Pre-Clinical Spinal Cord Injury Experiments},
booktitle = {Workshop on Ontologies and Data in Life Sciences
(ODLS 2017), Joint Workshops on Ontologies (JOWO)},
year = {2017},
address = {Bolzano, Italy},
month = {September},
pdf = {http://ceur-ws.org/Vol-2050/ODLS_paper_11.pdf},
url = {https://pub.uni-bielefeld.de/download/2913603/2913881},
internaltype = {workshop}
}
@inproceedings{Reiter2017,
author = {Nils Reiter and Sarah Schulz and Gerhard Kremer and
Roman Klinger and Gabriel Viehhauser and Jonas Kuhn},
title = {Teaching Computational Aspects in the Digital
Humanities Program at University of Stuttgart --
Intentions and Experiences},
booktitle = {Proceedings of the Workshop on Teaching NLP for
Digital Humanities (Teach4DH 2017) co-located with
GSCL 2017},
year = {2017},
pages = {43-48},
url = {http://ceur-ws.org/Vol-1918/reiter.pdf},
internaltype = {workshop}
}
@inproceedings{Thorne2017,
author = {Camilo Thorne and Roman Klinger},
title = {Towards Confidence Estimation for typed
Protein-Protein Relation Extraction},
booktitle = {Proceedings of the Biomedical NLP Workshop
associated with RANLP},
year = {2017},
address = {Varna, Bulgaria},
month = {September},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/W17-8008},
internaltype = {workshop}
}
@inproceedings{Hartung2017a,
author = {Matthias Hartung and Roman Klinger and Lars Vogel
and Franziska Schmidtke},
title = {Ranking Right-Wing Extremist Social Media Profiles
by Similarity to Democratic and Extremist Groups},
booktitle = {Proceedings of the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = 2017,
address = {Copenhagen, Denmark},
organization = {Workshop at Conference on Empirical Methods in
Natural Language Processing},
publisher = {Association for Computational Linguistics},
url = {https://www.aclanthology.org/W17-5204},
internaltype = {workshop}
}
@inproceedings{Schuff2017,
author = {Hendrik Schuff and Jeremy Barnes and Julian Mohme
and Sebastian Pad\'o and Roman Klinger},
title = {Annotation, Modelling and Analysis of Fine-Grained
Emotions on a Stance and Sentiment Detection Corpus},
booktitle = {Proceedings of the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = {2017},
address = {Copenhagen, Denmark},
organization = {Workshop at Conference on Empirical Methods in
Natural Language Processing},
publisher = {Association for Computational Linguistics},
pdf = {https://www.aclanthology.org/W17-5203/},
url = {http://www.ims.uni-stuttgart.de/data/ssec},
internaltype = {workshop}
}
@inproceedings{Barnes2017,
author = {Jeremy Barnes and Roman Klinger and Schulte im
Walde, Sabine},
title = {Assessing State-of-the-Art Sentiment Models on
State-of-the-Art Sentiment Datasets},
booktitle = {Proceedings of the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = {2017},
address = {Copenhagen, Denmark},
organization = {Workshop at Conference on Empirical Methods in
Natural Language Processing},
publisher = {Association for Computational Linguistics},
pdf = {https://www.aclanthology.org/W17-5202/},
url = {http://www.ims.uni-stuttgart.de/data/sota_sentiment},
internaltype = {workshop},
archiveprefix = {arXiv},
eprint = {1709.04219}
}
@inproceedings{Koeper2017,
author = {Maximilian K\"oper and Evgeny Kim and Roman Klinger},
title = {{IMS} at {EmoInt-2017}: Emotion Intensity Prediction
with Affective Norms, Automatically Extended
Resources and Deep Learning},
booktitle = {Proceedings of the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = {2017},
address = {Copenhagen, Denmark},
organization = {Workshop at Conference on Empirical Methods in
Natural Language Processing},
publisher = {Association for Computational Linguistics},
pdf = {https://www.aclanthology.org/W17-5206/},
url = {http://www.ims.uni-stuttgart.de/data/ims_emoint},
internaltype = {workshop}
}
@inproceedings{Kim2017a,
author = {Kim, Evgeny and Pad\'{o}, Sebastian and Klinger,
Roman},
title = {Investigating the Relationship between Literary
Genres and Emotional Plot Development},
booktitle = {Proceedings of the Joint SIGHUM Workshop on
Computational Linguistics for Cultural Heritage,
Social Sciences, Humanities and Literature},
month = {August},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
pages = {17-26},
url = {http://www.aclanthology.org/W17-2203},
internaltype = {workshop}
}
@inproceedings{Butzken2005,
author = {Miriam B\"utzken and Stefan Edelkamp and Abdelaziz
Elalaoui and Kenneth Kahl and Rachid Karmouni and
Roman Klinger and Khalid Lahiane and Andrea
Matuszewski and Tilman Mehler and Mohammed Nazih and
Michael Nelskamp and Arne Wiggers},
title = {{An Integrated Toolkit for Modern Action Planning}},
booktitle = {19th Workshop on New Results in Planning, Scheduling
and Design (PUK)},
year = {2005},
pages = {1-11},
url = {http://www.puk-workshop.de/puk2005/paper/1_puk1.pdf},
owner = {rklinger},
timestamp = {2006.12.13},
internaltype = {workshop}
}
@inproceedings{Bobic2012,
author = {Bobic, Tamara and Klinger, Roman and Thomas,
Philippe and Hofmann-Apitius, Martin},
title = {Improving Distantly Supervised Extraction of
Drug-Drug and Protein-Protein Interactions},
booktitle = {Proceedings of the Joint Workshop on Unsupervised
and Semi-Supervised Learning in NLP},
year = {2012},
pages = {35-43},
address = {Avignon, France},
month = {April},
publisher = {Association for Computational Linguistics},
url = {http://www.aclanthology.org/W12-0705},
internaltype = {workshop}
}
@inproceedings{Buschmeier2014,
author = {Buschmeier, Konstantin and Cimiano, Philipp and Klinger, Roman},
title = {An Impact Analysis of Features in a Classification Approach to Irony
Detection in Product Reviews},
booktitle = {Proceedings of the 5th Workshop on Computational Approaches to Subjectivity,
Sentiment and Social Media Analysis},
year = {2014},
pages = {42-49},
address = {Baltimore, Maryland},
month = {June},
publisher = {Association for Computational Linguistics},
url = {http://www.aclanthology.org/W14-2608},
internaltype = {workshop}
}
@inproceedings{Hartung2014,
author = {Hartung, Matthias and Klinger, Roman and Zwick,
Matthias and Cimiano, Philipp},
title = {Towards Gene Recognition from Rare and Ambiguous
Abbreviations using a Filtering Approach},
booktitle = {Proceedings of BioNLP 2014},
year = {2014},
pages = {118-127},
address = {Baltimore, Maryland},
month = {June},
publisher = {Association for Computational Linguistics},
url = {http://www.aclanthology.org/W14-3418},
internaltype = {workshop}
}
@inproceedings{kessler2015,
author = {Kessler, Wiltrud and Klinger, Roman and Kuhn, Jonas},
title = {Towards Opinion Mining from Reviews for the
Prediction of Product Rankings},
booktitle = {Proceedings of the 6th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social
Media Analysis},
year = {2015},
pages = {51-57},
address = {Lisboa, Portugal},
month = {September},
publisher = {Association for Computational Linguistics},
url = {http://aclanthology.org/W15-2908},
internaltype = {workshop}
}
@inproceedings{Klinger2013,
author = {Klinger, Roman and Cimiano, Philipp},
title = {Joint and Pipeline Probabilistic Models for
Fine-Grained Sentiment Analysis: Extracting Aspects,
Subjective Phrases and their Relations},
booktitle = {2013 IEEE 13th International Conference on Data
Mining Workshops (ICDMW)},
year = {2013},
pages = {937-944},
month = {Dec},
doi = {10.1109/ICDMW.2013.13},
pdf = {http://www.romanklinger.de/publications/joint-aspect-subjectivity-with-reference.pdf},
internaltype = {workshop}
}
@inproceedings{Klinger2011,
author = {Roman Klinger and Sebastian Riedel and Andrew
McCallum},
title = {Inter-Event Dependencies support Event Extraction
from Biomedical Literature},
booktitle = {Mining Complex Entities from Network and Biomedical
Data (MIND), European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery
in Databases (ECML PKDD)},
year = {2011},
url = {http://www.romanklinger.de/publications/klinger11interevent.pdf},
internaltype = {workshop}
}
@inproceedings{Kolarik2008,
author = {Corinna Kolarik and Roman Klinger and Christoph
M. Friedrich and Martin Hofmann-Apitius and Juliane
Fluck},
title = {{Chemical Names: Terminological Resources and
Corpora Annotation}},
booktitle = {{Workshop on Building and evaluating resources for
biomedical text mining (6th edition of the Language
Resources and Evaluation Conference)}},
year = {2008},
pages = {51-58},
address = {Marrakech, Morocco},
month = {May},
url = {http://www.romanklinger.de/publications/kolarik2008.pdf},
internaltype = {workshop}
}
@inproceedings{Paassen2014,
author = {Paassen, Benjamin and St\"{o}ckel, Andreas and
Dickfelder, Raphael and G\"{o}pfert, Jan Philip and
Brazda, Nicole and Kirchhoffer, Tarek and
M\"{u}ller, Hans Werner and Klinger, Roman and
Hartung, Matthias and Cimiano, Philipp},
title = {Ontology-based Extraction of Structured Information
from Publications on Preclinical Experiments for
Spinal Cord Injury Treatments},
booktitle = {Proceedings of the Third Workshop on Semantic Web
and Information Extraction},
year = {2014},
pages = {25-32},
address = {Dublin, Ireland},
month = {August},
publisher = {Association for Computational Linguistics and Dublin
City University},
url = {http://www.aclanthology.org/W14-6204},
internaltype = {workshop}
}
@inproceedings{Ruppenhofer2014,
author = {Josef Ruppenhofer and Roman Klinger and Julia Maria
Struß and Jonathan Sonntag and Michael Wiegand},
title = {{IGGSA Shared Tasks on German Sentiment Analysis}},
booktitle = {Workshop Proceedings of the 12th Edition of the
KONVENS Conference},
year = {2014},
editor = {Gertrud Faaß and Josef Ruppenhofer},
address = {Hildesheim, Germany},
month = {October},
publisher = {University of Hildesheim},
url = {https://ids-pub.bsz-bw.de/frontdoor/deliver/index/docId/8460/file/Ruppenhofer_et_al_IGGSA_Shared_Tasks_on_German_Sentiment_Analysis_2014.pdf},
internaltype = {workshop}
}
@inproceedings{Thomas2012,
author = {Philippe Thomas and Tamara Bobić and Ulf Leser and
Martin Hofmann-Apitius and Roman Klinger},
title = {Weakly Labeled Corpora as Silver Standard for
Drug-Drug and Protein-Protein Interaction},
booktitle = {Proceedings of the Workshop on Building and
Evaluating Resources for Biomedical Text Mining
(BioTxtM) on Language Resources and Evaluation
Conference (LREC)},
year = {2012},
address = {Istanbul, Turkey},
url = {http://www.romanklinger.de/publications/ppi-ddi.pdf},
internaltype = {workshop}
}
@inproceedings{Thomas2011a,
author = {Thomas, Philippe and Solt, Ill\'{e}s and Klinger,
Roman and Leser, Ulf},
title = {Learning Protein Protein Interaction Extraction
using Distant Supervision},
booktitle = {Proceedings of Workshop on Robust Unsupervised and
Semisupervised Methods in Natural Language
Processing},
year = {2011},
pages = {25-32},
address = {Hissar, Bulgaria},
month = {September},
url = {http://www.aclanthology.org/W11-3904},
internaltype = {workshop}
}