Browsing by Author "Pagel, Janis"

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    Detecting protagonists in German plays around 1800 as a classification task
    (2018) Reiter, Nils; Krautter, Benjamin; Pagel, Janis; Willand, Marcus
    In this paper, we aim at identifying protagonists in plays automatically. To this end, we train a classifier using various features and investigate the importance of each feature. A challenging aspect here is that the number of spoken words for a character is a very strong baseline. We can show, however, that a) the stage presence of characters and b) topics used in their speech can help to detect protagonists even above the baseline.
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    Enhancing character type detection using coreference information : experiments on dramatic texts
    (2024) Pagel, Janis; Kuhn, Jonas (Prof. Dr.)
    This thesis describes experiments on enhancing machine-learning based detection of literary character types in German-language dramatic texts by using coreference information. The thesis makes four major contributions to the research discourse of character type detection and coreference resolution for German dramatic texts: (i) a corpus of annotations of coreference on dramatic texts, called GerDraCor-Coref, (ii) a rule-based system to automatically resolve coreferences on dramatic texts, called DramaCoref, as well as experiments and analyses of results by using DramaCoref on GerDraCor-Coref, (iii) experiments on the automatic detection of three selected character types (title characters, protagonists and schemers) using machine-learning approaches, and (iv) experiments on utilizing the coreference information of (i) and (ii) for improving the performance of character type detection of (iii).
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    Klassifikation von Titelfiguren in deutschsprachigen Dramen und Evaluation am Beispiel von Lessings "Emilia Galotti"
    (2019) Krautter, Benjamin; Pagel, Janis
    Der Idee einer quantitativen und zugleich multidimensionalen Einteilung dramatischer Figuren folgend versuchen wir Titelfiguren im deutschsprachigen Drama automatisch zu bestimmen. Dazu fassen wir das Problem als Klassifikationsaufgabe, die mit maschinellen Lernverfahren bearbeitet wird. Als Features nutzen wir die gesprochenen Tokens der Figuren, deren Bühnenpräsenz, Netzwerkmetriken, Topic Modeling und einige Metadaten. Wir können zeigen, dass unser multidimensionales Modell sinnvolle Ergebnisse für die Klassifikation titelgebender Figuren liefert: MCC 0.66. Titelfiguren werden sehr zuverlässig erkannt (Recall 1.00), das Modell neigt jedoch zur Übergeneralisierung. Wir evaluieren diese Klassifikationsergebnisse anhand von Lessings „Emilia Galotti“.