TY - CHAP A1 - Baumann, Timo A1 - Hussein, Hussein A1 - Meyer-Sickendiek, Burkhard A1 - Elbeshausen, Jasper ED - Draude, Claude ED - Lange, Martin ED - Sick, Bernhard T1 - A Tool for Human-in-the-Loop Analysis and Exploration of (not only) Prosodic Classifications for Post-modern Poetry T2 - Informatik 2019 : 50 Jahre Gesellschaft für Informatik Workshop-Beiträge Fachtagung vom 23.-26. September 2019 in Kassel N2 - Data-based analyses are becoming more and more common in the Digital Humanities and tools are needed that focus human efforts on the most interesting and important aspects of exploration, analysis and annotation by using active machine learning techniques. We present our ongoing work on a tool that supports classification tasks for spoken documents (in our case: read-out post-modern poetry) using a neural networks-based classification backend and a web-based exploration and classification environment. KW - classification KW - data mining KW - free verse poetry KW - human-in-the-loop KW - rhythmical patterns Y1 - 2019 UR - https://dl.gi.de/bitstream/handle/20.500.12116/25047/paper03_08.pdf?sequence=1&isAllowed=y SN - 978-3-88579-689-3 SP - 151 EP - 156 PB - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Hussein, Hussein A1 - Meyer-Sickendiek, Burkhard A1 - Baumann, Timo ED - Birkholz, Peter ED - Stone, Simon T1 - How to identify elliptical poems within a digital corpus of auditory poetry T2 - Elektronische Sprachsignalverarbeitung 2019 (ESSV), Tagungsband der 30. Konferenz, Dresden, 3.-8. März 2019 N2 - Ellipses denote the omission of one or more grammatically necessary phrases. In this paper, we will demonstrate how to identify such ellipses as a rhythmical pattern in modern and postmodern free verse poetry by using data from lyrikline which contain the corresponding audio recording of each poem as spoken by the original author. We present a feature engineering approach based on literary analysis as well as a neural networks based approach for the identification of ellipses within the lines of a poem. A contrast class to the ellipsis is defined from poems consisting of complete and correct sentences. The feature-based approach used features derived from a parser such as verb, comma, and sentence ending punctuation. The classifier of neural networks is trained on the line level to integrate the textual information, the spoken recitation, and the pause information between lines, and to integrate information across the lines within the poem. A statistic analysis of poet's gender showed that 65% of all elliptical poems were written by female poets. The best results, calculated by the weighted F-measure, for the classification of ellipsis with the contrast class is 0.94 with the neural networks based approach. The best results for classification of elliptical lines is 0.62 with the feature-based approach. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:18-228-7-2579 UR - http://www.essv.de/paper.php?id=88 SN - 978-3-959081-57-3 SP - 247 EP - 254 PB - TUDpress CY - Dresden, Germany ER - TY - CHAP A1 - Hussein, Hussein A1 - Meyer-Sickendiek, Burkhard A1 - Baumann, Timo T1 - Identification of Concrete Poetry within a Modern-Poetry Corpus using Neural Networks T2 - Proceedings of the Quantitative Approaches to Versification Conference N2 - This work aims to discern the poetics of concrete poetry by using a corpus-based classification focusing on the two most important techniques used within concrete poetry: semantic decomposition and syntactic permutation. We demonstrate how to identify concrete poetry in modern and postmodern free verse. A class contrasting to concrete poetry is defined on the basis of poems with complete and correct sentences. We used the data from lyrikline, which contain both the written as well as the spoken form of poems as read by the original author. We explored two approaches for the identification of concrete poetry. The first is based on the definition of concrete poetry in literary theory by the extraction of various types of features derived from a parser, such as verb, noun, comma, sentence ending, conjunction, and asemantic material. The second is a neural network-based approach, which is theoretically less informed by human insight, as it does not have access to features established by scholars. This approach used the following inputs: textual information and the spoken recitation of poetic lines as well as information about pauses between lines. The results based on the neural network are more accurate than the feature-based approach. The best results, calculated by the weighted F-measure, for the classification of concrete poetry vis-à-vis the contrasting class is 0.96 Y1 - 2019 UR - https://versologie.cz/conference2019/proceedings/hussein-meyer-sickendiek-baumann.pdf SP - 95 EP - 104 CY - Prague, Czech Republic ER - TY - GEN A1 - Meyer-Sickendiek, Burkhard A1 - Hussein, Hussein A1 - Baumann, Timo T1 - From Fluency To Disfluency: Ranking Prosodic Features Of Poetry By Using Neural Networks T2 - Proceedings of the International Conference on Digital Humanities (DH 2019), Utrecht, the Netherlands 9-12 July, 2019 Y1 - 2019 U6 - https://doi.org/10.34894/EGZNMI ER -