Daniel Butzke


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Evaluation of Scientific Elements for Text Similarity in Biomedical Publications
Mariana Neves | Daniel Butzke | Barbara Grune
Proceedings of the 6th Workshop on Argument Mining

Rhetorical elements from scientific publications provide a more structured view of the document and allow algorithms to focus on particular parts of the text. We surveyed the literature for previously proposed schemes for rhetorical elements and present an overview of its current state of the art. We also searched for available tools using these schemes and applied four tools for our particular task of ranking biomedical abstracts based on text similarity. Comparison of the tools with two strong baselines shows that the predictions provided by the ArguminSci tool can support our use case of mining alternative methods for animal experiments.


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Bf3R at SemEval-2018 Task 7: Evaluating Two Relation Extraction Tools for Finding Semantic Relations in Biomedical Abstracts
Mariana Neves | Daniel Butzke | Gilbert Schönfelder | Barbara Grune
Proceedings of The 12th International Workshop on Semantic Evaluation

Automatic extraction of semantic relations from text can support finding relevant information from scientific publications. We describe our participation in Task 7 of SemEval-2018 for which we experimented with two relations extraction tools - jSRE and TEES - for the extraction and classification of six relation types. The results we obtained with TEES were significantly superior than those with jSRE (33.4% vs. 30.09% and 20.3% vs. 16%). Additionally, we utilized the model trained with TEES for extracting semantic relations from biomedical abstracts, for which we present a preliminary evaluation.