15 citations · 41 across the 17 of their papers we have counts for
22 papers
Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain
Annerose Eichel, Helena Schlipf, Sabine Schulte im Walde
We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task st…
What Drives the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations
Prisca Piccirilli, Sabine Schulte im Walde
Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives? For example, what drives people to say "gra…
Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality
Prisca Piccirilli, Sabine Schulte im Walde
Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity; prior work is however limited to the word and sentence levels,…
Lexical Semantic Change Discovery
Sinan Kurtyigit, Maike Park, Dominik Schlechtweg +2
While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In th…
More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods
Thomas Bott, Dominik Schlechtweg, Sabine Schulte im Walde
This paper presents a comparison of unsupervised methods of hypernymy prediction (i.e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hypo…
Explaining and Improving BERT Performance on Lexical Semantic Change Detection
Severin Laicher, Sinan Kurtyigit, Dominik Schlechtweg +2
Type- and token-based embedding architectures are still competing in lexical semantic change detection. The recent success of type-based models in SemEval-2020 Task 1 has raised th…
CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection
Severin Laicher, Gioia Baldissin, Enrique Castañeda +2
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit Average Pairwise Distance of token-based BERT…
OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
Jens Kaiser, Dominik Schlechtweg, Sabine Schulte im Walde
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit one of the earliest and most influential seman…
IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection
Jens Kaiser, Dominik Schlechtweg, Sean Papay +1
We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling. Our sy…
Simulating Lexical Semantic Change from Sense-Annotated Data
Dominik Schlechtweg, Sabine Schulte im Walde
We present a novel procedure to simulate lexical semantic change from synchronic sense-annotated data, and demonstrate its usefulness for assessing lexical semantic change detectio…
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP
Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde +1
Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pa…
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
Dominik Schlechtweg, Anna Hätty, Marco del Tredici +1
We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and…
Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling
Dominik Schlechtweg, Cennet Oguz, Sabine Schulte im Walde
We simulate first- and second-order context overlap and show that Skip-Gram with Negative Sampling is similar to Singular Value Decomposition in capturing second-order co-occurrenc…
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to th…
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
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…
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness
Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
We present two novel datasets for the low-resource language Vietnamese to assess models of semantic similarity: ViCon comprises pairs of synonyms and antonyms across word classes,…
Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change
Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann
We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models…
Distribution-based Prediction of the Degree of Grammaticalization for German Prepositions
Dominik Schlechtweg, Sabine Schulte im Walde
We test the hypothesis that the degree of grammaticalization of German prepositions correlates with their corpus-based contextual dispersion measured by word entropy. We find that…
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers,…
Hierarchical Embeddings for Hypernymy Detection and Directionality
Kim Anh Nguyen, Maximilian Köper, Sabine Schulte im Walde +1
We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypic…
German in Flux: Detecting Metaphoric Change via Word Entropy
Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus +2
This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We also build…
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
We propose a novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word simi…