Detecting protagonists in German plays around 1800 as a classification task

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

Who Knows What in German Drama? A Composite Annotation Scheme for Knowledge Transfer. Annotation, Evaluation, and Analysis

The distribution of knowledge among characters is established as an important feature for drama analysis. Many turning points in plays are triggered by a knowledge transfer. However, knowledge transfers in plays have not yet been targeted in a formal or computational way. This paper aims at developing a framework to digitally model processes of knowledge dissemination concerning family and love relations among fictional characters in plays. We approach this as an annotation task and introduce how our composite annotation scheme models knowledge transfers among characters. We present preliminary results and discuss the question of measuring inter-annotator agreement, the calculation of which is not yet standardised for this type of annotation. Finally, we showcase an analysis of the annotated knowledge transfers on Günderrode’s 1805 play, Udohla

The X-ray emission from Nova V382 Velorum: I. The hard component observed with BeppoSAX

We present BeppoSAX observations of Nova Velorum 1999 (V382 Vel), done in a broad X-ray band covering 0.1-300 keV only 15 days after the discovery and again after 6 months. The nova was detected at day 15 with the BeppoSAX instruments in the energy range 1.8-10 keV and we attribute the emission to shocks in the ejecta. The plasma temperature was kT~6 keV and the unabsorbed flux was F(x)~4.3 x 10(-11) erg/cm**2/s. The nebular material was affected by high intrinsic absorption of the ejecta. 6 months after after the outburst, the intrinsic absorption did not play a role, the nova had turned into a bright supersoft source, and the hot nebular component previously detected had cooled to a plasma temperature kT<=1 keV. No emission was detected in either observation above 20 keV.Comment: 1 tex file, 2 figures as .ps, and 1 .sty file of MNRA

X-ray emission from classical and recurrent-novae observed with ROSAT

We have analysed 350 pointed and serendipitous observations of 108 different classical and recurrent novae in outburst and in quiescence, contained in the ROSAT archive. One aim was to search for super-soft X-ray sources and we found only 3 of them among post-novae. Thus, the super-soft X-ray phase of novae is relatively short lived (up to 10 years) and is observed only for up to 20% of novae. Most classical and recurrent novae instead emit hard X-rays (in the ROSAT band) in the first months after the outburst, with peak X-ray luminosity of a few times 10(33) erg/s. The emission, which we attribute to shocks in the nova ejecta, lasts at least 2 years and even much longer under special circumstances (like preexisting circumstellar material, or a prolonged wind phase). We also investigate X-ray emission due to accretion in quiescent novae. Only 11 out of 81 Galactic classical and recurrent novae were detected. The average X-ray uminosity is not higher than for dwarf novae, and some novae are variable in X-rays on time scales of years.Comment: tex file of the text and 8 figure

A community-sourced glossary of open scholarship terms

Open scholarship has transformed research, introducing a host of new terms in the lexicon of researchers. The Framework of Open and Reproducible Research Teaching (FORRT) community presents a crowd-sourced glossary of open scholarship terms to facilitate education and effective communication between experts and newcomers

Klassifikation von Titelfiguren in deutschsprachigen Dramen und Evaluation am Beispiel von Lessings "Emilia Galotti"

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“