Tim Fischer


2024

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AnnoPlot: Interactive Visualizations of Text Annotations
Elisabeth Fittschen | Tim Fischer | Daniel Brühl | Julia Spahr | Yuliia Lysa | Phuoc Thang Le
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This paper presents AnnoPlot, a web application designed to analyze, manage, and visualize annotated text data.Users can configure projects, upload datasets, and explore their data through interactive visualization of span annotations with scatter plots, clusters, and statistics. AnnoPlot supports various transformer models to compute high-dimensional embeddings of text annotations and utilizes dimensionality reduction algorithms to offer users a novel 2D view of their datasets.A dynamic approach to dimensionality reduction allows users to adjust visualizations in real-time, facilitating category reorganization and error identification. The proposed application is open-source, promoting transparency and user control.Especially suited for the Digital Humanities, AnnoPlot offers a novel solution to address challenges in dynamic annotation datasets, empowering users to enhance data integrity and adapt to evolving categorizations.

2023

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From Qualitative to Quantitative Research: Semi-Automatic Annotation Scaling in the Digital Humanities
Fynn Petersen-Frey | Tim Fischer | Florian Schneider | Isabel Eiser | Gertraud Koch | Chris Biemann
Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)

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The D-WISE Tool Suite: Multi-Modal Machine-Learning-Powered Tools Supporting and Enhancing Digital Discourse Analysis
Florian Schneider | Tim Fischer | Fynn Petersen-Frey | Isabel Eiser | Gertraud Koch | Chris Biemann
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

This work introduces the D-WISE Tool Suite (DWTS), a novel working environment for digital qualitative discourse analysis in the Digital Humanities (DH). The DWTS addresses limitations of current DH tools induced by the ever-increasing amount of heterogeneous, unstructured, and multi-modal data in which the discourses of contemporary societies are encoded. To provide meaningful insights from such data, our system leverages and combines state-of-the-art machine learning technologies from Natural Language Processing and Com-puter Vision. Further, the DWTS is conceived and developed by an interdisciplinary team ofcultural anthropologists and computer scientists to ensure the tool’s usability for modernDH research. Central features of the DWTS are: a) import of multi-modal data like text, image, audio, and video b) preprocessing pipelines for automatic annotations c) lexical and semantic search of documents d) manual span, bounding box, time-span, and frame annotations e) documentation of the research process.

2022

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Measuring Faithfulness of Abstractive Summaries
Tim Fischer | Steffen Remus | Chris Biemann
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)

2019

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LT Expertfinder: An Evaluation Framework for Expert Finding Methods
Tim Fischer | Steffen Remus | Chris Biemann
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Expert finding is the task of ranking persons for a predefined topic or search query. Finding experts for a specified area is an important task and has attracted much attention in the information retrieval community. Most approaches for this task are evaluated in a supervised fashion, which depend on predefined topics of interest as well as gold standard expert rankings. Famous representatives of such datasets are enriched versions of DBLP provided by the ArnetMiner projet or the W3C Corpus of TREC. However, manually ranking experts can be considered highly subjective and detailed rankings are hardly distinguishable. Evaluating these datasets does not necessarily guarantee a good or bad performance of the system. Particularly for dynamic systems, where topics are not predefined but formulated as a search query, we believe a more informative approach is to perform user studies for directly comparing different methods in the same view. In order to accomplish this in a user-friendly way, we present the LT Expert Finder web-application, which is equipped with various query-based expert finding methods that can be easily extended, a detailed expert profile view, detailed evidence in form of relevant documents and statistics, and an evaluation component that allows the qualitative comparison between different rankings.