Andreas Hotho


2023

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Jack-Ryder at SemEval-2023 Task 5: Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions
Dirk Wangsadirdja | Jan Pfister | Konstantin Kobs | Andreas Hotho
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper, we describe our approach to the clickbait spoiling task of SemEval 2023.The core idea behind our system is to leverage pre-trained models capable of Question Answering (QA) to extract the spoiler from article texts based on the clickbait title without any task-specific training. Since oftentimes, these titles are not phrased as questions, we automatically rephrase the clickbait titles as questions in order to better suit the pretraining task of the QA-capable models. Also, to fit as much relevant context into the model’s limited input size as possible, we propose to reorder the sentences by their relevance using a semantic similarity model. Finally, we evaluate QA as well as text generation models (via prompting) to extract the spoiler from the text. Based on the validation data, our final model selects each of these components depending on the spoiler type and achieves satisfactory zero-shot results. The ideas described in this paper can easily be applied in fine-tuning settings.

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Pointer Networks: A Unified Approach to Extracting German Opinions
Julia Wunderle | Jan Pfister | Andreas Hotho
Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)

2022

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LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance
Stefan Heil | Karina Kopp | Albin Zehe | Konstantin Kobs | Andreas Hotho
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity. The task of the competition consisted of the development of a model, capable of determining the similarity between pairs of multilingual news articles. To address this challenge, we evaluated the Word Mover’s Distance in conjunction with word embeddings from ConceptNet Numberbatch and term frequencies of WorldLex, as well the Sentence Mover’s Distance based on sentence embeddings generated by pretrained transformer models of Sentence-BERT. To facilitate the comparison of multilingual articles with Sentence-BERT models, we deployed a Neural Machine Translation system. All our models achieve stable results in multilingual similarity estimation without learning parameters.

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WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices
Dirk Wangsadirdja | Felix Heinickel | Simon Trapp | Albin Zehe | Konstantin Kobs | Andreas Hotho
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each news article sentence, it searches the most similar sentence from the other article and computes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.

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SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi
Jan Pfister | Sebastian Wankerl | Andreas Hotho
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70%. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.

2021

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Detecting Scenes in Fiction: A new Segmentation Task
Albin Zehe | Leonard Konle | Lea Katharina Dümpelmann | Evelyn Gius | Andreas Hotho | Fotis Jannidis | Lucas Kaufmann | Markus Krug | Frank Puppe | Nils Reiter | Annekea Schreiber | Nathalie Wiedmer
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions. A scene here is a segment of the text where time and discourse time are more or less equal, the narration focuses on one action and location and character constellations stay the same. The corpus we describe consists of German-language dime novels (550k tokens) that have been annotated in parallel, achieving an inter-annotator agreement of gamma = 0.7. Baseline experiments using BERT achieve an F1 score of 24%, showing that the task is very challenging. An automatic scene segmentation paves the way towards processing longer narrative texts like tales or novels by breaking them down into smaller, coherent and meaningful parts, which is an important stepping stone towards the reconstruction of plot in Computational Literary Studies but also can serve to improve tasks like coreference resolution.

2020

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Where to Submit? Helping Researchers to Choose the Right Venue
Konstantin Kobs | Tobias Koopmann | Albin Zehe | David Fernes | Philipp Krop | Andreas Hotho
Findings of the Association for Computational Linguistics: EMNLP 2020

Whenever researchers write a paper, the same question occurs: “Where to submit?” In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83% for AI papers and 95% in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.

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Improving Sentiment Analysis with Biofeedback Data
Daniel Schlör | Albin Zehe | Konstantin Kobs | Blerta Veseli | Franziska Westermeier | Larissa Brübach | Daniel Roth | Marc Erich Latoschik | Andreas Hotho
Proceedings of LREC2020 Workshop "People in language, vision and the mind" (ONION2020)

Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.

2019

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Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks
Albin Zehe | Lena Hettinger | Stefan Ernst | Christian Hauptmann | Andreas Hotho
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection. We build on an existing deep learning approach for sentence classification based on a Convolutional Neural Network. Modifying the original model with additional layers to increase its expressiveness and finally building an ensemble of multiple versions of the model, we obtain an accuracy of 67.52% and an F1 score of 73.78% on the main test dataset. We also report on additional experiments incorporating handcrafted features into the CNN and using it as a feature extractor for a linear SVM.

2018

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ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings
Lena Hettinger | Alexander Dallmann | Albin Zehe | Thomas Niebler | Andreas Hotho
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). We compare two models for classification, a C-LSTM which utilizes only word embeddings and an SVM that also takes handcrafted features into account. To adapt to the domain of science we train word embeddings on scientific papers collected from arXiv.org. The hand-crafted features consist of lexical features to model the semantic relations as well as the entities between which the relation holds. Classification of Relations using Embeddings (ClaiRE) achieved an F1 score of 74.89% for the first subtask and 78.39% for the second.

2004

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Clustering Concept Hierarchies from Text
Philipp Cimiano | Andreas Hotho | Steffen Staab
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)