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In Tackling the Toolkit, we focus on the methodological innovations, challenges, obstacles and even shortcomings associated with applying quantitative methods to poetry specifically and poetics more broadly. Using tools including natural language processing, web ontologies, similarity detection devices and machine learning, our contributors explore...
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... types TTR a b # types TTR a b # tokens 0.99 -0.74 0.31 -0.19 > 0.99 -0.92 0.94 -0.82 # types -0.66 0.32 -0.21 -0.92 0.92 -0.81 TTR -0.44 0.33 -0.82 0.71 a -0.97 -0.91 Table 5: Pearson correlation coefficients between basic text characteristics and MAL parameters rate for both groups of texts. However, the curves representing model (1) for prose tend to be higher than the ones for poetry (as can be seen in Figure 1 for two texts). ...
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... final conceptual model included both descriptive and bibliographic aspects of the poetic works. It also included information about textual transmission, prosodic,literary and rhetorical features, poetic structures, significant publication elements and relationships with music(Curado Malta, Malta et al. 2017) (see Figure 1). ...
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... shown in Figure 1, based on their stress patterns, the four authors can be split into two groups that respectively represent the Renaissance and the Baroque styles: Garcilaso and Herrera show similar patterns and are both Renaissance figures; Góngora and Lope constitute the Baroque group. The most striking difference between the two groups relates to the frequency of stressed syllables in the seventh and tenth positions. ...
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... values were compared to a theoretical model constructed by randomizing the order of stanzas in each poem 10,000 times. As Figure 1 shows, the differences between the expected and observed values were not statistically significant for most of the poems (α = 0.05). We were therefore unable to confirm any overall tendency to group stanzas based on their line endings. ...
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... kinds of tile plots were used in this study: positive (left-hand column of Figure 1), negative (right-hand column of Figure 1) and contrasting (Figure 2). ...
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... kinds of tile plots were used in this study: positive (left-hand column of Figure 1), negative (right-hand column of Figure 1) and contrasting (Figure 2). ...
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... plot is not symmetrical because percentages are calculated relative to the total number of verses considered in each row. In the first positive tile plot of Figure 1, for example, the cell in the first row of the second column and the cell in the second row of the first column have the values of 19% and 12% respectively. Nevertheless they both reflect the same total number of verses-537. ...
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... means that when they may be stressed, they will likely be involved in a stress clash that allows them to become weakened in favor of the fourth, sixth or eighth syllables. This can be seen in the positive tile plots for Figure 1: in the rows for the fifth and seventh syllables, the sixth syllable may almost always be stressed as well. The poet GLM shows a marked preference for a stressed second syllable; this is visible in his positive tile plot where a possible stress on the first and the third syllables is very frequently associated with a stress clash with the second syllable. ...
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... thus, filtered our data, we obtained the results given in Table 2 (for the full data, see footnote 5). Figure 1 is a line-plot which represents the 20 most common topolexes. These are sorted in descending order by frequency (x-axis) and number of recurrences (y-axis) and plotted on a logarithmic scale. ...
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... line plot in Figure 5 depicts the 20 most common topolexes in each text, as sorted in descending order by frequency (x-axis) and number of recurrences (y-axis) and plotted on a logarithmic scale (cf. Figure 1). As the graph illustrates, although The Theogony is not identical to the Homeric works, it is closer to them than it is to Apollonius's text. ...
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... We repeated the experiment with blocks containing 350 lines each. As Figures 9 and 10 show, 7 both stylometric indicators again yielded good results. ...
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... We also achieved reasonably good results for both stylometric indicators when using smaller blocks of 100 lines each. To make this work, however, we had to extend the ranges to the 350 most common words and the 900 most common topolexes, as illustrated in Figures 11 and 12 (see footnote 7). ...
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... Finally, when using blocks smaller than 50 lines, we found that no matter how much we extended the frequency ranges (and even when we went as high as the 10,000 most common words/topolexes), we failed to obtain satisfactory results for either the MFW or the topolexis indicator (the best results can be seen in Figures 13 and 14; see footnote 7). ...
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... Finnic languages (see Figure 1) share a specific poetic-musical tradition that is characterised by stichic diction (no stanzas or rhymes), interdependent use of alliteration and parallelism and a syllabic metre with a trochaic core. 1 In the English-language scholarship, this tradition has various names of which the most common are Finnic oral poetry, Kalevalaic poetry and runosong although researchers have also noted the shortcomings of these various terms ( Kallio et al. 2017). What is distinctive about this poetic form is its use across a wide variety of sung genres (e.g., epic, lyric and ritual songs, occasional songs, charms and improvisations) and also to some degree in colloquial speech and spoken word genres such as proverbs, sayings and riddles (Krikmann 1997;Kuusi 1994;Tampere et al. 2017). ...
