Auflistung nach Autor:in "Schaer, Philipp"
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- ZeitschriftenartikelA System for Probabilistic Linking of Thesauri and Classification Systems(KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Posch, Lisa; Schaer, Philipp; Bleier, Arnim; Strohmaier, MarkusThis paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system. For creating the links, we build on the Polylingual Labeled Topic Model (PLL-TM) (Posch et al., in KI 2015: advances in artificial intelligence, 2015). PLL-TM identifies probable thesaurus descriptors for each class in the classification system by using information from the natural language text of documents, their assigned thesaurus descriptors and their designated classes. The links are then presented to users of the system in an interactive visualization, providing them with an automatically generated overview of the relations between the thesaurus and the classification system.
- KonferenzbeitragAutomated Statement Extraction from Press Briefings(BTW 2023, 2023) Keller, Jüri; Bittkowski, Meik; Schaer, PhilippScientific press briefings are a valuable information source. They consist of alternating expert speeches, questions from the audience and their answers. Therefore, they can contribute to scientific and fact-based media coverage. Even though press briefings are highly informative, extracting statements relevant to individual journalistic tasks is challenging and time-consuming.To support this task, an automated statement extraction system is proposed. Claims are used as the main feature to identify statements in press briefing transcripts. The statement extraction task is formulated as a four-step procedure. First, the press briefings are split into sentences and passages, then claim sentences are identified with a single-label multi-class sequence classification. Subsequently, topics are detected, and the sentences are filtered to improve the coherence and assess the length of the statements.The results indicate that claim detection can be used to identify statements in press briefings. While many statements can be extracted automatically with this system, they are not always as coherent as needed to be understood without context and may need further review by knowledgeable persons.
- ZeitschriftenartikelEditorial(Datenbank-Spektrum: Vol. 20, No. 1, 2020) Schaer, Philipp; Berberich, Klaus; Härder, Theo
- ZeitschriftenartikelErratum zu: Editorial(Datenbank-Spektrum: Vol. 21, No. 2, 2021) Schaer, Philipp; Berberich, Klaus; Härder, Theo
- ZeitschriftenartikelEvaluation Infrastructures for Academic Shared Tasks(Datenbank-Spektrum: Vol. 20, No. 1, 2020) Schaible, Johann; Breuer, Timo; Tavakolpoursaleh, Narges; Müller, Bernd; Wolff, Benjamin; Schaer, PhilippAcademic search systems aid users in finding information covering specific topics of scientific interest and have evolved from early catalog-based library systems to modern web-scale systems. However, evaluating the performance of the underlying retrieval approaches remains a challenge. An increasing amount of requirements for producing accurate retrieval results have to be considered, e.g., close integration of the system’s users. Due to these requirements, small to mid-size academic search systems cannot evaluate their retrieval system in-house. Evaluation infrastructures for shared tasks alleviate this situation. They allow researchers to experiment with retrieval approaches in specific search and recommendation scenarios without building their own infrastructure. In this paper, we elaborate on the benefits and shortcomings of four state-of-the-art evaluation infrastructures on search and recommendation tasks concerning the following requirements: support for online and offline evaluations, domain specificity of shared tasks, and reproducibility of experiments and results. In addition, we introduce an evaluation infrastructure concept design aiming at reducing the shortcomings in shared tasks for search and recommender systems.
- KonferenzbeitragReliable Rules for Relation Extraction in a Multimodal Setting(BTW 2023, 2023) Engelmann, Björn; Schaer, PhilippWe present an approach to extract relations from multimodal documents using a few training data. Furthermore, we derive explanations in the form of extraction rules from the underlying model to ensure the reliability of the extraction. Finally, we will evaluate how reliable (high model fidelity) extracted rules are and which type of classifier is suitable in terms of F1 Score and explainability. Our code and data are available at https://osf.io/dn9hm/?view_only=7e65fd1d4aae44e1802bb5ddd3465e08.