klinger.bib
@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{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{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{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{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{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}
}
@article{troiano2022theories,
title = {From theories on styles to their transfer in text:
Bridging the gap with a hierarchical survey},
doi = {10.1017/S1351324922000407},
journal = {Natural Language Engineering},
publisher = {Cambridge University Press},
author = {Troiano, Enrica and Velutharambath, Aswathy and
Klinger, Roman},
year = {2022},
pages = {1–60},
archiveprefix = {arXiv},
eprint = {2110.15871},
internaltype = {journal}
}
@proceedings{wassa-2022-approaches,
title = {Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis},
editor = {Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Barriere, Valentin and
Tafreshi, Shabnam and
Alqahtani, Sawsan and
Sedoc, Jo{\~a}o and
Klinger, Roman and
Balahur, Alexandra},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.wassa-1.0},
internaltype = {edited}
}