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Auflistung nach Schlagwort "Relation Extraction"

1 - 3 von 3
  • Konferenzbeitrag
    Relation Extraction from Environmental Law Text Using Natural Language Understanding
    (EnviroInfo 2022, 2022) Thimm, Heiko; Schneider, Phil
    In the last decades the highly active area of environmental legislation has produced a vast amount of text documents that contain laws and regulations enacted by various types of rule setters. This large body of legal text documents is still growing with an increasing speed. In order to assure compliance with the regulations, today, corporate specialist spend a lot of time with the reviewing and assessment of these documents. It seems that through the use of text processing assistance tools these important corporate environmental compliance management tasks can be completed in less time. To develop corresponding assistance tools has been the broader goal of this work in which initial text processing experiments with a common Natural Language Understanding pipeline are described. The obtained results confirm that in order to extract meaningful relations from text documents of the environmental legislation area, domain-specific processing techniques that are tailored to the specific language and format of legal text are required.
  • Konferenzbeitrag
    Reliable Rules for Relation Extraction in a Multimodal Setting
    (BTW 2023, 2023) Engelmann, Björn; Schaer, Philipp
    We 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.
  • Konferenzbeitrag
    Social Relation Extraction from Chatbot Conversations: A Shortest Dependency Path Approach
    (SKILL 2019 - Studierendenkonferenz Informatik, 2019) Glas, Markus
    Digital dialog systems, also known as chatbots, often lack in the sense of a human-like and individualized interaction. The ability to learn someoneŠs social relations during conversations can lead to more personal responses and therefore to a more human-like and diverse conversation. In this work we present S-REX, a comparison method for extracting social relations from chatbot conversations. The implemented approach uses information from the shortest dependency path in combination with state-of-the-art natural language processing models for entity recognition and semantic word vectors. The method is evaluated on two conversational datasets and achieves results close to more complex neural network methods without the need of extensive training.