Search Results for author: Roman Klinger

Found 81 papers, 15 papers with code

A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors

1 code implementation FNP (COLING) 2020 Felix Armbrust, Henry Schäfer, Roman Klinger

We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the “Management’s Discussion and Analysis of Financial Conditions and Results of Operations” (MD&A) section of 10-K (yearly) and 10-Q (quarterly) filings.

“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words

no code implementations WASSA (ACL) 2022 Valentino Sabbatino, Enrica Troiano, Antje Schweitzer, Roman Klinger

This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e. g., morphological and phonological patterns.

Entity-Level Sentiment: More than the Sum of Its Parts

1 code implementation4 Jul 2024 Egil Rønningstad, Roman Klinger, Erik Velldal, Lilja Øvrelid

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.

Sentence Sentiment Analysis

Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception

no code implementations15 Mar 2024 Aswathy Velutharambath, Amelie Wührl, Roman Klinger

As the argumentation stems from genuine belief, it may be unlikely to exhibit the linguistic properties associated with deception or lying.


Understanding Fine-grained Distortions in Reports of Scientific Findings

no code implementations19 Feb 2024 Amelie Wührl, Dustin Wright, Roman Klinger, Isabelle Augenstein

Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions.

English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts

no code implementations5 Feb 2024 Patrick Bareiß, Roman Klinger, Jeremy Barnes

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.

Emotion Classification Emotion Recognition +3

What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification

no code implementations2 Feb 2024 Amelie Wührl, Yarik Menchaca Resendiz, Lara Grimminger, Roman Klinger

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.

Anatomy Claim Verification +3

Topic Bias in Emotion Classification

no code implementations14 Dec 2023 Maximilian Wegge, Roman Klinger

when funeral events are over-represented for instances labeled with sadness, despite the emotion of pride being more appropriate here.

Classification Emotion Classification

Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches

no code implementations5 Sep 2023 Roman Klinger

(1) Emotions are events; and this perspective is the fundament in natural language processing for emotion role labeling.

Emotion Classification Emotion Recognition

Emotion-Conditioned Text Generation through Automatic Prompt Optimization

no code implementations9 Aug 2023 Yarik Menchaca Resendiz, Roman Klinger

We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure.

Conditional Text Generation Few-Shot Text Classification +4

Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions

no code implementations26 Jul 2023 Yarik Menchaca Resendiz, Roman Klinger

They put the assessment of the situation on the spot, for instance regarding the own control or the responsibility for what happens.

Text Generation

UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection

1 code implementation5 Jun 2023 Aswathy Velutharambath, Roman Klinger

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.

Cross-corpus Deception Detection +1

Automatic Emotion Experiencer Recognition

no code implementations26 May 2023 Maximilian Wegge, Roman Klinger

We show that experiencer detection in text is a challenging task, with a precision of . 82 and a recall of . 56 (F1 =. 66).

Emotion Classification Emotion Recognition +2

An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking

no code implementations11 Apr 2023 Amelie Wührl, Lara Grimminger, Roman Klinger

This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims.

Entity Linking Fact Checking +3

Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets

no code implementations ArgMining (ACL) 2022 Amelie Wührl, Roman Klinger

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.

Fact Checking

Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

no code implementations10 Jun 2022 Enrica Troiano, Laura Oberländer, Roman Klinger

We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories.

Emotion Recognition text-classification +1

x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations

no code implementations LREC 2022 Enrica Troiano, Laura Oberländer, Maximilian Wegge, Roman Klinger

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).

Emotion Classification Emotion Recognition

"splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words

no code implementations24 Feb 2022 Valentino Sabbatino, Enrica Troiano, Antje Schweitzer, Roman Klinger

This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e. g., morphological and phonological patterns.

Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users

no code implementations WASSA (ACL) 2022 Anne Kreuter, Kai Sassenberg, Roman Klinger

We investigate this approach for personality profiling, and evaluate BERT classifiers fine-tuned on such psychometric test items for the big five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and analyze various augmentation strategies regarding their potential to address the challenges coming with such a small corpus.

Data Augmentation

Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference

1 code implementation NAACL 2022 Emīls Kadiķis, Vaibhav Srivastav, Roman Klinger

We do this by testing how well the pre-trained models perform on the \alpha{}nli task when just comparing sentence embeddings with cosine similarity to what the performance that is achieved when training a classifier on top of these embeddings.

Common Sense Reasoning Model Selection +3

On the Complementarity of Images and Text for the Expression of Emotions in Social Media

no code implementations WASSA (ACL) 2022 Anna Khlyzova, Carina Silberer, Roman Klinger

The emotions of anger and sadness are best predicted with a multimodal model, while text alone is sufficient for disgust, joy, and surprise.

From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey

no code implementations29 Oct 2021 Enrica Troiano, Aswathy Velutharambath, Roman Klinger

With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task.

Style Transfer Text Generation

Emotion Stimulus Detection in German News Headlines

no code implementations KONVENS (WS) 2021 Bao Minh Doan Dang, Laura Oberländer, Roman Klinger

Emotion stimulus extraction is a fine-grained subtask of emotion analysis that focuses on identifying the description of the cause behind an emotion expression from a text passage (e. g., in the sentence "I am happy that I passed my exam" the phrase "passed my exam" corresponds to the stimulus.).

Emotion Recognition Sentence +1

Emotion Recognition under Consideration of the Emotion Component Process Model

no code implementations KONVENS (WS) 2021 Felix Casel, Amelie Heindl, Roman Klinger

It states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency.

Emotion Classification Emotion Recognition

Constraining Linear-chain CRFs to Regular Languages

1 code implementation ICLR 2022 Sean Papay, Roman Klinger, Sebastian Padó

However, the CRF's Markov assumption makes it impossible for CRFs to represent distributions with \textit{nonlocal} dependencies, and standard CRFs are unable to respect nonlocal constraints of the data (such as global arity constraints on output labels).

Semantic Role Labeling Structured Prediction

Claim Detection in Biomedical Twitter Posts

no code implementations NAACL (BioNLP) 2021 Amelie Wührl, Roman Klinger

We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase).

Fact Checking Fake News Detection +2

Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled

no code implementations EACL (WASSA) 2021 Enrica Troiano, Sebastian Padó, Roman Klinger

When humans judge the affective content of texts, they also implicitly assess the correctness of such judgment, that is, their confidence.

Lost in Back-Translation: Emotion Preservation in Neural Machine Translation

no code implementations COLING 2020 Enrica Troiano, Roman Klinger, Sebastian Pad{\'o}

Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world.

Diversity Machine Translation +3

Dissecting Span Identification Tasks with Performance Prediction

no code implementations EMNLP 2020 Sean Papay, Roman Klinger, Sebastian Padó

Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text.

Chunking NER

Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline

1 code implementation WS 2020 David Helbig, Enrica Troiano, Roman Klinger

We propose the task of emotion style transfer, which is particularly challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise) are on the fence between content and style.

Language Modelling Sentence +2

Appraisal Theories for Emotion Classification in Text

no code implementations COLING 2020 Jan Hofmann, Enrica Troiano, Kai Sassenberg, Roman Klinger

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).

Emotion Classification General Classification +1

PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry

1 code implementation LREC 2020 Thomas Haider, Steffen Eger, Evgeny Kim, Roman Klinger, Winfried Menninghaus

Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context.

Emotion Classification Emotion Recognition

Automatic Section Recognition in Obituaries

no code implementations LREC 2020 Valentino Sabbatino, Laura Bostan, Roman Klinger

Obituaries contain information about people's values across times and cultures, which makes them a useful resource for exploring cultural history.

Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning

no code implementations6 Sep 2019 Deniz Cevher, Sebastian Zepf, Roman Klinger

We use off-the-shelf tools for emotion detection in audio and face and compare that to a neural transfer learning approach for emotion recognition from text which utilizes existing resources from other domains.

Multimodal Emotion Recognition Transfer Learning

An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling

no code implementations WS 2019 Evgeny Kim, Roman Klinger

Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.

Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study

1 code implementation24 Jun 2019 Jeremy Barnes, Roman Klinger

As expected, the choice of annotated source language for projection to a target leads to better results for source-target language pairs which are similar.

Machine Translation Sentence +1

An Analysis of Emotion Communication Channels in Fan Fiction: Towards Emotional Storytelling

no code implementations6 Jun 2019 Evgeny Kim, Roman Klinger

Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.

Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis

no code implementations WS 2019 Laura Bostan, Roman Klinger

Adjective phrases like "a little bit surprised", "completely shocked", or "not stunned at all" are not handled properly by currently published state-of-the-art emotion classification and intensity prediction systems which use pre-dominantly non-contextualized word embeddings as input.

Emotion Classification Emotion Recognition +2

Adversarial Training for Satire Detection: Controlling for Confounding Variables

no code implementations NAACL 2019 Robert McHardy, Heike Adel, Roman Klinger

We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source.

General Classification Knowledge Base Population +1

IEST: WASSA-2018 Implicit Emotions Shared Task

no code implementations WS 2018 Roman Klinger, Orphée De Clercq, Saif M. Mohammad, Alexandra Balahur

Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions.

An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

no code implementations31 Aug 2018 Florian Strohm, Roman Klinger

We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification.

Emotion Classification General Classification +1

A Survey on Sentiment and Emotion Analysis for Computational Literary Studies

no code implementations9 Aug 2018 Evgeny Kim, Roman Klinger

Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions.

Emotion Recognition Sentiment Analysis

Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions

no code implementations COLING 2018 Evgeny Kim, Roman Klinger

We aim at filling this gap and present a publicly available corpus based on Project Gutenberg, REMAN (Relational EMotion ANnotation), manually annotated for spans which correspond to emotion trigger phrases and entities/events in the roles of experiencers, targets, and causes of the emotion.

Emotion Recognition

An Analysis of Annotated Corpora for Emotion Classification in Text

no code implementations COLING 2018 Laura-Ana-Maria Bostan, Roman Klinger

Based on this aggregation, we perform the first cross-corpus classification experiments in the spirit of future research enabled by this paper, in order to gain insight and a better understanding of differences of models inferred from the data.

Classification Cross-corpus +2

Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains

1 code implementation COLING 2018 Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde

Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.

Domain Adaptation Sentiment Analysis +1

Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains

1 code implementation12 Jun 2018 Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde

Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.

Domain Adaptation Sentiment Analysis +1

Predicting Disease-Gene Associations using Cross-Document Graph-based Features

no code implementations26 Sep 2017 Hendrik ter Horst, Matthias Hartung, Roman Klinger, Matthias Zwick, Philipp Cimiano

In the context of personalized medicine, text mining methods pose an interesting option for identifying disease-gene associations, as they can be used to generate novel links between diseases and genes which may complement knowledge from structured databases.


Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets

no code implementations WS 2017 Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde

We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes).

Sentiment Analysis Word Embeddings

IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning

no code implementations WS 2017 Maximilian K{\"o}per, Evgeny Kim, Roman Klinger

Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.

Emotion Recognition Sentence +1

Towards Confidence Estimation for Typed Protein-Protein Relation Extraction

no code implementations RANLP 2017 Camilo Thorne, Roman Klinger

In this paper, we discuss this task and propose different approaches for confidence estimation and a pipeline to evaluate such methods.

Entity Linking Named Entity Recognition (NER) +2

SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German

no code implementations LREC 2016 Mario S{\"a}nger, Ulf Leser, Steffen Kemmerer, Peter Adolphs, Roman Klinger

This corpus consists of 1, 760 annotated application reviews from the Google Play Store with 2, 487 aspects and 3, 959 subjective phrases.

Sentiment Analysis

The USAGE review corpus for fine grained multi lingual opinion analysis

no code implementations LREC 2014 Roman Klinger, Philipp Cimiano

Contributing to this situation, this paper describes the Bielefeld University Sentiment Analysis Corpus for German and English (USAGE), which we offer freely to the community and which contains the annotation of product reviews from Amazon with both aspects and subjective phrases.

Opinion Mining Sentiment Analysis

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