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Found 8 papers, 5 papers with code
MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain
In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Factuality Detection using Machine Translation -- a Use Case for German Clinical Text
Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed.
MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain
We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities.
Evaluating German Transformer Language Models with Syntactic Agreement Tests
Most of the studies were conducted for the English language, however.
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task
TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE).
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models.
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products
Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as supply chain monitoring and market research.
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge.
