Search Results for author: Dominik Schlechtweg

Found 28 papers, 13 papers with code

Regression Analysis of Lexical and Morpho-Syntactic Properties of Kiezdeutsch

no code implementations EACL (VarDial) 2021 Diego Frassinelli, Gabriella Lapesa, Reem Alatrash, Dominik Schlechtweg, Sabine Schulte im Walde

Kiezdeutsch is a variety of German predominantly spoken by teenagers from multi-ethnic urban neighborhoods in casual conversations with their peers.


DiaWUG: A Dataset for Diatopic Lexical Semantic Variation in Spanish

no code implementations LREC 2022 Gioia Baldissin, Dominik Schlechtweg, Sabine Schulte im Walde

We provide a novel dataset – DiaWUG – with judgements on diatopic lexical semantic variation for six Spanish variants in Europe and Latin America.

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

1 code implementation13 May 2022 Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg

We present the first shared task on semantic change discovery and detection in Spanish and create the first dataset of Spanish words manually annotated for semantic change using the DURel framework (Schlechtweg et al., 2018).

Change Detection

Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Dominik Schlechtweg, Enrique Castaneda, Jonas Kuhn, Sabine Schulte im Walde

We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences.

Stochastic Block Model

Lexical Semantic Change Discovery

1 code implementation ACL 2021 Sinan Kurtyigit, Maike Park, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde

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.

Change Detection

More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods

1 code implementation Findings (ACL) 2021 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 hyponym).

Challenges for Computational Lexical Semantic Change

no code implementations19 Jan 2021 Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg, Haim Dubossarsky

The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics.

OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection

no code implementations6 Nov 2020 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.

Change Detection

IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection

no code implementations SEMEVAL 2020 Jens Kaiser, Dominik Schlechtweg, Sean Papay, Sabine Schulte im Walde

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.

Change Detection

SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

2 code implementations SEMEVAL 2020 Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, Nina Tahmasebi

Lexical Semantic Change detection, i. e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics.

Change Detection

Predicting Degrees of Technicality in Automatic Terminology Extraction

no code implementations ACL 2020 Anna H{\"a}tty, Dominik Schlechtweg, Michael Dorna, Sabine Schulte im Walde

While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied.

Term Extraction Word Embeddings

CCOHA: Clean Corpus of Historical American English

no code implementations LREC 2020 Reem Alatrash, Dominik Schlechtweg, Jonas Kuhn, Sabine Schulte im Walde

Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics.

Shared task: Lexical semantic change detection in German (Student Project Report)

no code implementations21 Jan 2020 Adnan Ahmad, Kiflom Desta, Fabian Lang, Dominik Schlechtweg

Recent NLP architectures have illustrated in various ways how semantic change can be captured across time and domains.

Change Detection

Simulating Lexical Semantic Change from Sense-Annotated Data

no code implementations9 Jan 2020 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 detection models.

Change Detection

A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains

1 code implementation ACL 2019 Dominik Schlechtweg, Anna Hätty, Marco del Tredici, Sabine Schulte im Walde

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 semantic sense changes across domains.

Term Extraction

Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling

1 code implementation WS 2019 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-occurrence information, while Pointwise Mutual Information is agnostic to it.

SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction

no code implementations SEMEVAL 2019 Anna H{\"a}tty, Dominik Schlechtweg, Sabine Schulte im Walde

We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts.

Term Extraction

Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change

no code implementations NAACL 2018 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 of lexical semantic change.

Distribution-based Prediction of the Degree of Grammaticalization for German Prepositions

no code implementations14 Apr 2018 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.

German in Flux: Detecting Metaphoric Change via Word Entropy

1 code implementation CONLL 2017 Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus, Sabine Schulte im Walde, Daniel Hole

This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change.

Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

1 code implementation EACL 2017 Vered Shwartz, Enrico Santus, Dominik Schlechtweg

The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution.

Hypernym Discovery

Exploitation of Co-reference in Distributional Semantics

no code implementations LREC 2016 Dominik Schlechtweg

It is because of this convenience that most current state-of-the-art-models of distributional semantics operate on raw text, although there have been successful attempts to integrate other kinds of―e. g., syntactic―information to improve distributional semantic models.

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