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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.
A large proportion of (post)-modern poetry contains no or hardly any punctuation. In our contribution, we will investigate how well punctuation information can be recovered for postmodern poetry based on the information contained in the text and speech of free verse poems. We use the world's largest corpus of spoken (post-)modern poetry from our partner lyrikline which contains the corresponding audio recording of each poem as spoken by the original author and features translations for many of the poems. We identify lines that contain a phrase break in the middle of the poetic line, which may already be helpful for philological analysis on one hand, and identify the position of the break in the line on the other hand. We select those poetic lines that contain one or more punctuation characters that typically indicate a phrase break in poetry (.,;:!?/) somewhere in the middle (rather than only at the end of the line) as our target class. We train a neural network (bidirectional recurrent neural network (RNN) based on gated recurrent units (GRU) with attention) that combines audio and textual features to identify the punctuation with the goal of applying it to reconstruct them within a corpus of unpunctuated poems. Our results clearly indicate that speech is helpful for recovering the constituency structure of post-modern poetry that is partially obfuscated by missing punctuation.
Our paper focuses on the computational analysis of “readout poetry” (german: Hördichtung) – recordings of poets reading their own work – with regards to the most important type of this genre, the modern “sound poetry” (german: Lautdichtung). Whereas “readout poetry” often uses normal words and sentences, the “sound poetry”, developed by dadaistic poets like Hugo Ball and Kurt Schwitters or concrete poets like Ernst Jandl, Oskar Pastior, or Bob Cobbing, combines the “microparticles of the human voice” like the segments in Ernst Jandls sound poem “schtzngrmm” (“schtzngrmm / schtzngrmm / tttt / tttt / grrrmmmmm / tttt / sch / tzngrmm”). Within the genre of sound poetry, there are two main forms: The lettristic and the syllabic decomposition. A short anecdote will explain this difference: The dadaist Raoul Hausmann developed the lettristic sound poetry in his early dadaistic poem “fmsbw” from 1918. This is said to have inspired his successor Schwitters, whose famous “Ursonate” [The Sonata in Primal Speech] begins with the words “Fümms bö wö tää zää Uu”. With the “Ursonate”, Schwitters developed a syllabic variation of the lettristic poems of Hausmann. The paper shows how to train a bidirectional LSTM network in order to differ between these “dadaistic” sound poems and the “normal” read out poems. In a further step, we will also show how to distinguish between the lettristic and the syllabic decomposition. Based on a bidirectional LSTM network that reads encodings of the character sequence in the poem and uses the output of each directional layer, we identify poems of the sound poetry genre and differentiate between its two types of compositions. The classification results of sound poetry vs. other poetry as well as lettristic vs. syllabic decomposition are with a high performance, yielding a f-scores of 0.86 and 0.84, respectively.
One of the most important patterns in ancient as well as modern poetry is the enjambment, the continuation of a sentence beyond the end of a line, couplet, or stanza. The paper reports first activities towards the development of a digital tool to analyze the accentuation of poetic enjambments in readout poetry. The aim in this contribution is to recognize two forms of enjambment (emphasized and unemphasized) in poems using audio and text data. We use data from lyrikline which is a major online portal for spoken poetry whereas poems are read aloud by the original authors. We identified by hermeneutical means based on literary analysis a total of 69 poems being characteristic for the use of enjambments in modern and postmodern German poetry and train classifiers to differentiate the emphasized/unemphasized ategorization. A remarkable result of our automated analyses (and to our knowledge the first data-driven analysis of this kind) is the identification of a cultural difference in the accentuation of enjambments: statistically speaking, poets from the former GDR tend to emphasize the enjambment, whereas poets from the FRG do not. We use features derived from speech-to-text alignment and statistical parsing information such as pause lengths, number of lines with verbs, and number of lines with punctuation. The best classification results, calculated by the F-measure, for the both types of enjambment (emphasized/unemphasized) is 0.69.
After overcoming the traditional metrics, modern and postmodern poetry developed a large variety of ‘free verse prosodies’ that falls along a spectrum from a more fluent to a more disfluent and choppy style. We present a method, grounded in philological analysis and theories on cognitive (dis)fluency, to analyze this ‘free verse spectrum’ into six classes of poetic styles as well as to differentiate three types of poems with enjambments. We use a model for automatic prosodic analysis of spoken free verse poetry which uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We then analyze and fine-tune the model with a particular focus on enjambments and in two ways: we drill down on classification performance by analyzing whether the model focuses on similar traits of poems as humans would, specifically, whether it internally builds a notion of enjambment. We find that our model is similarly good as humans in finding enjambments; however, when we employ the model for classifying enjambment-dominated poem types, it does not pay particular attention to those lines. Adding enjambment labels to the training only marginally improves performance, indicating that all other lines are similarly informative for the model.
We present our research on computer-supported analysis of prosodic styles in post-modern poetry. Our project is unique in making use of both the written as well as the spoken form of the poem as read by the original author. In particular, we use speech and natural language processing technology to align speech and text and to perform textual analyses. We then explore, based on literary theory, the quantitative value of various types of features in differentiating various prosodic classes of post-modern poetry using machine-learning techniques. We contrast this feature-driven approach with a theoretically less informed neural networks-based approach and explore the relative strengths of both models, as well as how to integrate higher-level knowledge into the NN. In this paper, we give an overview of our project, our approach, and particularly focus on the challenges encountered and lessons learned in our interdisciplinary endeavour. The classification results of the rhythmical patterns (six classes) using NN-based approaches are better than by feature-based approaches.
We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.
Modern and post-modern free verse poems feature a large and complex variety in their poetic prosodies that falls along a continuum from a more fluent to a more disfluent and choppy style. As the poets of modernism overcame rhyme and meter, they oriented themselves in these two opposing directions, creating a free verse spectrum that calls for new analyses of prosodic forms. We present a method, grounded in philological analysis and current research on cognitive (dis)fluency, for automatically analyzing this spectrum. We define and relate six classes of poetic styles (ranging from parlando to lettristic decomposition) by their gradual differentiation. Based on this discussion, we present a model for automatic prosodic classification of spoken free verse poetry that uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We evaluate our model on a large corpus of German author-read post-modern poetry and find that classes can reliably be differentiated, reaching a weighted f-measure of 0.73, when combining textual and phonetic evidence. In our further analyses, we validate the model’s decision-making process, the philologically hypothesized continuum of fluency and investigate the relative importance of various features.