klinger.bib

@inproceedings{Bagdon2024,
  author = {Christopher Bagdon and Prathamesh Karmalkar and Harsha Gurulingappa and Roman Klinger},
  title = {"You are an expert annotator": Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling},
  booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2024},
  month = {June},
  address = {Mexico City, Mexico},
  publisher = {Association for Computational Linguistics},
  note = {accepted},
  internaltype = {conferenceproc},
  url = {https://www.romanklinger.de/publications/BagdonNAACL2024.pdf}
}
@inproceedings{Wuehrl2024b,
  title = {Understanding Fine-grained Distortions in Reports of
                  Scientific Findings},
  author = {Amelie W\"uhrl and Dustin Wright and Roman Klinger
                  and Isabelle Augenstein},
  booktitle = {Findings of the Association for Computational
                  Linguistics: ACL 2024},
  month = {August},
  year = {2024},
  address = {Bangkok, Thailand},
  publisher = {Association for Computational Linguistics},
  pdf = {https://arxiv.org/pdf/2402.12431.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}
}