Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Schlechtweg, Dominik"'
Autor:
Artemova, Ekaterina, Tsvigun, Akim, Schlechtweg, Dominik, Fedorova, Natalia, Tilga, Sergei, Obmoroshev, Boris
Training and deploying machine learning models relies on a large amount of human-annotated data. As human labeling becomes increasingly expensive and time-consuming, recent research has developed multiple strategies to speed up annotation and reduce
Externí odkaz:
http://arxiv.org/abs/2411.04637
This paper explores using GPT-3.5 and GPT-4 to automate the data annotation process with automatic prompting techniques. The main aim of this paper is to reuse human annotation guidelines along with some annotated data to design automatic prompts for
Externí odkaz:
http://arxiv.org/abs/2407.04130
There has been a surge of interest in computational modeling of semantic change. The foci of previous works are on detecting and interpreting word senses gained over time; however, it remains unclear whether the gained senses are covered by dictionar
Externí odkaz:
http://arxiv.org/abs/2406.00656
Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are
Externí odkaz:
http://arxiv.org/abs/2404.00176
We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The con
Externí odkaz:
http://arxiv.org/abs/2403.18024
This study addresses the task of Unknown Sense Detection in English and Swedish. The primary objective of this task is to determine whether the meaning of a particular word usage is documented in a dictionary or not. For this purpose, sense entries a
Externí odkaz:
http://arxiv.org/abs/2403.02285
Autor:
Schlechtweg, Dominik, Virk, Shafqat Mumtaz, Sander, Pauline, Sköldberg, Emma, Linke, Lukas Theuer, Zhang, Tuo, Tahmasebi, Nina, Kuhn, Jonas, Walde, Sabine Schulte im
We present the DURel tool that implements the annotation of semantic proximity between uses of words into an online, open source interface. The tool supports standardized human annotation as well as computational annotation, building on recent advanc
Externí odkaz:
http://arxiv.org/abs/2311.12664
Publikováno v:
Kutuzov, Andrei Fedorova, Mariia Schlechtweg, Dominik Arefev, Nikolay . Enriching Word Usage Graphs with Cluster Definitions. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 2024, 6189-6198 European Language Resources Association
Externí odkaz:
http://hdl.handle.net/10852/111276
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). The task is divided in t
Externí odkaz:
http://arxiv.org/abs/2205.06691
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 this paper, we propose a shift of focus from change detection to change
Externí odkaz:
http://arxiv.org/abs/2106.03111