Dirk Von Gruenigen

Also published as: Dirk von Grünigen


pdf bib
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation
Jan Deriu | Katsiaryna Mlynchyk | Philippe Schläpfer | Alvaro Rodrigo | Dirk von Grünigen | Nicolas Kaiser | Kurt Stockinger | Eneko Agirre | Mark Cieliebak
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database, called Operation Trees (OT). This representation allows us to invert the annotation process without loosing flexibility in the types of queries that we generate. Furthermore, it allows for fine-grained alignment of the tokens to the operations. Thus, we randomly generate OTs from a context free grammar and annotators just have to write the appropriate question and assign the tokens. We compare our corpus OTTA (Operation Trees and Token Assignment), a large semantic parsing corpus for evaluating natural language interfaces to databases, to Spider and LC-QuaD 2.0 and show that our methodology more than triples the annotation speed while maintaining the complexity of the queries. Finally, we train a state-of-the-art semantic parsing model on our data and show that our dataset is a challenging dataset and that the token alignment can be leveraged to significantly increase the performance.


pdf bib
Twist Bytes - German Dialect Identification with Data Mining Optimization
Fernando Benites | Ralf Grubenmann | Pius von Däniken | Dirk von Grünigen | Jan Deriu | Mark Cieliebak
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018. The goal was to identify to which out of four dialects spoken in German speaking part of Switzerland a sentence belonged to. We adopted two different meta classifier approaches and used some data mining insights to improve the preprocessing and the meta classifier parameters. Especially, we focused on using different feature extraction methods and how to combine them, since they influenced very differently the performance of the system. Our system achieved second place out of 8 teams, with a macro averaged F-1 of 64.6%.


pdf bib
Potential and Limitations of Cross-Domain Sentiment Classification
Jan Milan Deriu | Martin Weilenmann | Dirk Von Gruenigen | Mark Cieliebak
Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media

In this paper we investigate the cross-domain performance of a current state-of-the-art sentiment analysis systems. For this purpose we train a convolutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore, we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus. Our results show that more sophisticated approaches are required to train a system that works equally well on various domains.