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Clemens Fernau - Galgenfrist Nacht TV Movie Stefan. Himself - Experte Literatur. TV Series Himself - Episode 1. Himself - Narrator voice. Himself as Axel Millberg. Edit Personal Details Publicity Listings: In a similar study on affective words in Romanian and Russian, Sokolova and Bobicev captured the word form similarity by classification of words according to their emotion tags.
The results suggest that the form of words allows for a reliable classification of emotion. Evidence for the psychological reality of this sound-meaning correspondence comes from behavioral experiments. Wiseman and van Peer , for instance, revealed that when German and Brazilian participants were asked to produce fantasy words corresponding either to the emotions experienced at a wedding or at a funeral, they tended to use similar consonants for respective emotional states, and this was independent of their native language. In an attempt to examine the universality of this phenomenon, Auracher et al.
Their results were consistent with the results of Wiseman and van Peer and confirmed the relationship between the emotional tone happy vs. More recently, Myers-Schulz et al. In several analyses of English poetry and lyrics, Whissell has proposed that most of the basic sounds of English have emotional connotations attached to them see, for instance, Whissell, In sum, the use of stylistic elements in a text, such as foregrounded phonological salience can enhance aesthetic and emotional effects on reading experience.
On the other hand, the perceived similarity or analogy between the phonological forms of those salient elements and their meaning, or their emotional impacts on the reader, as assumed by the semiotic notion of phonological iconicity, can be a reliable source to predict a part of the emotional and aesthetic qualities of a given text. All the above mentioned theoretical claims or empirical approaches to phonological iconicity encounter the following problem: They assume that single sublexical units already relate to semantics below the lexical level in that they either possess an iconic value on their own, or directly convey meaning via a basic grounding of semantics or emotion at the level of single sounds.
Thus, how could a single sublexical unit serve as a linguistic sign conveying specific semantic meaning if it almost necessarily will be present in any text, regardless of its meaning? A plausible tentative answer to this question is: Notably, this is the point of view adopted by approaches trying to assign emotional values to sublexical units on a purely empirical base e. But what kind of information regarding the frequency of occurrence of single sublexical units could be extracted from single texts standing in the focus of interest?
To extract the salient sublexical units within a text, we developed a probabilistic model. The basic idea is to weight the frequencies of occurrences of a sublexical unit in a text by comparison to a linguistic corpus serving as a reference. Both single phonemes and sub-syllabic segments e. According to foregrounding theory, the foregrounded pattern in a literary text or in poetry deviates from a norm, either through replication or through parallelism. In his neurocognitive model of literary reading, Jacobs , assumes that not the deviation by itself, but the ratio between deviation and standard, between foregrounded and backgrounded elements, is the crucial factor for emotional and aesthetic experiences of literary reading.
To proceed on the assumption that these norms originate from everyday spoken language, a corpus that is to be used as a reference should be representative of everyday language. Recent studies have shown that word frequency calculated from corpora based on films and television subtitles can better account for reading performance than the traditional word frequency based on books and newspapers, since the language used in subtitles greatly approximates everyday language interactions with objects and other people ; evidence comes from: German Brysbaert et al.
They reported that the best quality of frequency measures for German regarding their correlation with behavioral word processing data is observed for SUBTLEX-DE; a corpus of 25 Million German words consisting of movie and television subtitles.
As the idea of phonological iconicity takes account of the phonological feature of language rather than orthography, each text to be analyzed should be converted into phonetic notation before analyzing it. To phonemize the text automatically, an extensive lexicon deals with known words and a letter-to-sound conversion algorithm with unknown words. On that account, we have split the corpus into small units, each including approximately characters 1. Translating more than units through MARY, parsing resulted XMLs and then integrating them in one dataset, we have generated a complete phonemized corpus for the German language; a useful resource for future research in the area of psycholinguistics.
The following steps of analyses will focus on relative syllabic position of phonemes. Based on internal structure of a syllable Figure 1 and a list of all 19 vowels in German 16 monophthongs and 3 diphthongs each sub-syllabic unit has been segmented and its frequency of occurrence in the corpus has been counted. Additionally, the sum of all sub-syllabic units and all phonemes existing in the corpus have been calculated in order to obtain the relative frequency of occurrence of each unit Table 1.
Since the significance of the use of a certain sub-syllabic unit depends on both its relative frequency of occurrence and the length of a given text, the intended model has to be able to predict the expected frequency and its standard deviation according to the corpus as a function of relative frequency and text length. Given the frequency of occurrence of a certain sub-syllabic unit in the corpus f u , c , the text length n text , and the corpus' length n c , the expected frequency of occurrence of this certain unit in a given text would be:.
That is, we consider the whole text as a collection of gaps which could be filled by any sub-syllabic units contributing to the formation of the whole text.
Return to Book Page. All poems used for the present analyses contain shared rhymes across endings of lines. Representation of workflow and modules employed in the tool. Calculation of standard deviation for each set of pulling. Looking at the brains behind figurative language - a quantitative meta-analysis of neuroimaging studies on metaphor, idiom, and irony processing.
A random text is simulated as random binary sequences …. Assuming that the occurrence of each unit in each position in the text is independent of the other units which is not necessarily given for language and that the probability that it succeeds in filling the position is equal to its relative frequency, one can consider the text as several Bernoulli trials with a binomial distribution. The standard deviation of a binomial distribution is:.
Given the fact that only specific arrangements of sub-syllabic units can contribute to meaningful words, it is obvious that the distribution of sub-syllabic units in a text does not have completely random characteristics as preconditioned by a binomial distribution. However, this distribution can help to form a rough estimate about the characteristics of standard deviation and its dependency on both input factors i.
To calculate standard deviations more precisely, we chose an empirical approach by pulling numerous chunks of sub-syllabic units from the corpus. For a text with a certain length, a text sample with the same length is randomly pulled from the corpus and the frequency of occurrence of all sub-syllabic units is counted in this sample.
Since the samples should be representative for a larger population, this procedure is repeated with replacement for 1 Million times for each specific length of text. It's worth pointing out that the corpus includes almost 25 Million words and more than 2 Million sentences. We opted to let all chunks start with the beginning of a sentence, because this best represents normal language.
In consequence, 2 Million different chunks can be pulled from the corpus. For each set of pulling i. 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.
This procedure of pulling is repeated for different lengths of texts, starting with the minimal size of 50 units for the first step, sampling different lengths with steps of 50 units each i. The analysis of all obtained curves shows, as expected, similar characteristics to a binomial distribution. Based on the mathematical equation for calculating the standard deviation of binomial distribution, we construct a curve that has the best fit to the series of our data points.
Note that the curves in Figure 4 represent standard deviation as a function of text length alone, and not of relative frequency. In fact, each phoneme as each sub-syllabic unit has a certain relative frequency, which means, the factor frequency doesn't appear as an input parameter, but rather as a constant value which is integrated in the function.
In so doing, we could obtain a number of single functions each of which can predict the standard deviation of a certain sub-syllabic unit or a phoneme dependent on the text length.
In order to obtain a general model for the prediction of any sub-syllabic units or phonemes, we compiled our all data in a three dimensional space as following: In this three dimensional space and, again, based on binomial distribution, we constructed a surface for the best fit to our data Figure 5. The 3D-model for prediction of standard deviation of sublexical units as a function of text length and frequency. Though the accuracy of this latter model is less than accuracy of single functions for each unit, having a general model with text length and frequency as inputs simplifies the determination of standard deviation.
It's worth mentioning that this empirical approach is not needed for determining of expected value, because the calculation of expected value, which is based on formula 1, does not assume a particular type of distribution as it was the case for the calculation of standard deviation. Based on the aforementioned model, we developed the computer linguistic tool Emophon which is a flexible tool for research and analysis of literary texts at the sublexical level. Emophon allows a step-by-step processing with an access to partial processing results.
All sub-syllabic units and phonemes of a given text phonemized through MARY are found and segmented. By means of the frequency value of each unit signed as f Ph in the representation and the text length calculated by tool , the integrated model provides a correspondent expected value and standard deviation for each unit.
The number of each existing sub-syllabic unit and phoneme in the text is compared with the confidence interval provided by the model. Results are outputted as both graphic diagrams and numerical values of the degree to which the confidence interval is exceeded or not. The sublexical salience in the text can be extracted and shown for structures at the following levels by simply selecting the corresponding option given in the GUI:. In the following we will first demonstrate and validate the functionality of Emophon and then show how it can be used as a linguistic instrument for analyzing and extracting the phonological salient units in a text.
To validate the functionality of Emophon we used a particular poem for which phonological deviation is known to some degree: In sound poetry, phonological aspects of a poem are foregrounded. In this genre of poetry, linguistic meaning waives in whole or in substantial part and the language is purely formal and can be seen as mere sound material. The poem was first converted to phonetic notation by using MARY.
After reading of the phonemized text by the tool, one can optionally select one of the above-mentioned levels of seeking salience in the text phonemes, onsets, nucleus, etc. The graphical demonstration of results concerning the phoneme level is presented in Figure 8. The number of existing phonemes, the expected value and the confidence interval for each phoneme in the text based on calculated standard deviation are represented in the diagram.
Phonemes that were significantly more frequent than expected and consequently exceeded the confidence interval are signed with an asterisk. Expected values of each phoneme are marked with the blue line, confidence intervals with red. In addition, the exact degree to which each phoneme exceeds the confidence interval is outputted by the tool and represented in Table 2. As predicted, all of three mentioned phonemes i. This is due to the fact that the expected value and the confidence interval for each phoneme positively correlate with its relative frequency in the corpus, which is low in this case.
Similar analyses can be conducted at the all above-mentioned levels to extract the salient sub-syllabic units in the text. Figure 9 demonstrates results of such an analysis for sub-syllabic onsets for the same poem. Similar to the previous analysis of phonemes, a large portion of existing onsets is marked as salient. To illustrate the potential use of Emophon as a linguistic instrument for analyzing the phonological salient units in texts following an exemplary hypothesis, we focus on the theory of foregrounding assuming that poetic language deviates from norms characterizing ordinary language use.
We hypothesize that this deviation is observable at the phonological level and that it can be measured by the numbers of salient phonological units as provided by our tool. To this end, we chose 20 classical poems of approximately equal length number of characters with space from different German poets.
All these poems are characterized by the two classical patterns of metric alignment and syllabic rhymes at the end of two lines either following an AABB or an ABAB pattern. In particular, these syllabic rhymes should lead to an increase in frequencies of occurrence of syllabic nuclei and codas in these examples of lyrical language when compared to everyday common language use. As control texts, we chose 20 text passages—matching the lyrical texts in length—that had appeared in different online German newspapers as respective first articles on the day of analysis see Appendix for a complete list of poems and newspaper articles.
The subjects of these articles vary topically ranging from political to local news. After converting all texts in phonetic notion and analyzing them by the tool, we documented phonological salient units at 4 different sublexical levels: For a statistical comparison between the two groups of texts, we defined two measures indicating the degree of phonological salience for each text: Thus, we obtained 8 indicators for each text, all of which can serve to compare poems and newspaper articles. The values of these indicators for each text according to the tool performance are represented in Table 3.
Numeric representation of the sublexical salient units for all of 20 poems and newspaper texts. Applying t -tests on these 8 indicators, we assessed the likelihood that the means for the two types of texts poems vs. Similarly to the number of salient units, there was a significant effect of text's type on the absolute sum of segments positioned outside the confidence interval: The means of the number and the absolute sum of salient onsets did not significantly differ between two groups: The wrong words are highlighted.
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