10 citations · 17 across the 8 of their papers we have counts for
9 papers
Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer Models
Zein Shaheen, Gerhard Wohlgenannt, Dmitry Mouromtsev
Zero-shot cross-lingual transfer is an important feature in modern NLP models and architectures to support low-resource languages. In this work, We study zero-shot cross-lingual tr…
Large Scale Legal Text Classification Using Transformer Models
Zein Shaheen, Gerhard Wohlgenannt, Erwin Filtz
Large multi-label text classification is a challenging Natural Language Processing (NLP) problem that is concerned with text classification for datasets with thousands of labels. W…
Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures
Zein Shaheen, Gerhard Wohlgenannt, Bassel Zaity +2
Generating coherent, grammatically correct, and meaningful text is very challenging, however, it is crucial to many modern NLP systems. So far, research has mostly focused on Engli…
A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data
Gerhard Wohlgenannt, Dmitry Mouromtsev, Dmitry Pavlov +2
With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages. Two approaches…
Word Similarity Datasets for Thai: Construction and Evaluation
Ponrudee Netisopakul, Gerhard Wohlgenannt, Aleksei Pulich
Distributional semantics in the form of word embeddings are an essential ingredient to many modern natural language processing systems. The quantification of semantic similarity be…
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain
Gerhard Wohlgenannt, Ariadna Barinova, Dmitry Ilvovsky +1
Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but…
Russian Language Datasets in the Digitial Humanities Domain and Their Evaluation with Word Embeddings
Gerhard Wohlgenannt, Artemii Babushkin, Denis Romashov +3
In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learni…
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings
Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky +2
In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models. The first ste…
Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata
Gerhard Wohlgenannt, Nikolay Klimov, Dmitry Mouromtsev +3
One of the big challenges in Linked Data consumption is to create visual and natural language interfaces to the data usable for non-technical users. Ontodia provides support for di…