Extracting salient sublexical units from written texts: "Emophon," a corpus-based approach to phonological iconicity

Abstract

A GROWING BODY OF LITERATURE IN PSYCHOLOGY, LINGUISTICS, AND THE NEUROSCIENCES HAS PAID INCREASING ATTENTION TO THE UNDERSTANDING OF THE RELATIONSHIPS BETWEEN PHONOLOGICAL REPRESENTATIONS OF WORDS AND THEIR MEANING: a phenomenon also known as phonological iconicity. In this article, we investigate how a text's intended emotional meaning, particularly in literature and poetry, may be reflected at the level of sublexical phonological salience and the use of foregrounded elements. To extract such elements from a given text, we developed a probabilistic model to predict the exceeding of a confidence interval for specific sublexical units concerning their frequency of occurrence within a given text contrasted with a reference linguistic corpus for the German language. Implementing this model in a computational application, we provide a text analysis tool which automatically delivers information about sublexical phonological salience allowing researchers, inter alia, to investigate effects of the sublexical emotional tone of texts based on current findings on phonological iconicity.

Keywords: foregrounding; neurocognitive poetics; phonological iconicity; sound symbolism; text analysis tool.

Figure 1

Representation of the structure of a syllable (σ) using the example of the German word “Gras”.

Figure 2

A random text is simulated as random binary sequences 000000100 …. The symbol “1” appears with probability p and models a successful occurrence of a certain sub-syllabic unit in a text (in this example the phoneme ℧), and the symbol “0” accounts for its unsuccessful occurrence with probability 1-p.

Figure 3

Calculation of standard deviation for each set of pulling. Note that the relative frequency of each sub-syllabic unit is constant giving one function for each unit.

Figure 4

Binomial-based model for prediction of standard deviation using the examples of “pf” and “tS”.

Figure 5

The 3D-model for prediction of standard deviation of sublexical units as a function of text length and frequency.

Figure 6

Representation of workflow and modules employed in the tool.

Figure 7

Hugo Ball's poem “Totenklage”.

Figure 8

Diagram of phonemes in the poem “Totenklage” as outputted by the tool. Expected values of each phoneme are marked with the blue line, confidence intervals with red.

Figure 9

Diagram of “salient” syllabic onsets in the poem “Totenklage” as outputted by the tool. ^*Denoting onsets that were significantly more (or less) frequent than expected.