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Tab. 56: Keywords that most frequently evoke non-perceptual frames. nouns are targets and adjectives are sources, then “it should follow that senses richer in nouns are more likely to be targets, while senses richer in adjectives are more likely to be sources” (Strik Lievers 2015: 87). Likewise, Ronga (2016: 48) notes that synesthetic metaphors in most cases contain a head, which is typi- cally a noun, and a modifier, which is most often an adjective. The multiformity of metaphors in texts is also noted by Goatly (1997) and Cameron (2003). It follows clearly from Goatly’s (1997) analysis of examples from the literature and the Bank of English that metaphors are expressed not only by nouns, but by other parts of speech as well. Some analyses show that verbs are more frequently used metaphorically (to evoke metaphorical source domains) than nouns (Cameron 2003; Deignan 2005; Pragglejaz Group 2007; Steen et al. 2010; Sullivan 2018). Cameron (2003: 88-89) compares the number of metaphors expressed by dif- ferent linguistic forms, which enables her to determine that almost 50 % of metaphors come in the form of verbs. Metaphorical expressions involving nouns (of the type A=B, e.g., Man is a wolf) make up no more than 5 % of the corpus.

Table 61 Tab. 56: Keywords that most frequently evoke non-perceptual frames. nouns are targets and adjectives are sources, then “it should follow that senses richer in nouns are more likely to be targets, while senses richer in adjectives are more likely to be sources” (Strik Lievers 2015: 87). Likewise, Ronga (2016: 48) notes that synesthetic metaphors in most cases contain a head, which is typi- cally a noun, and a modifier, which is most often an adjective. The multiformity of metaphors in texts is also noted by Goatly (1997) and Cameron (2003). It follows clearly from Goatly’s (1997) analysis of examples from the literature and the Bank of English that metaphors are expressed not only by nouns, but by other parts of speech as well. Some analyses show that verbs are more frequently used metaphorically (to evoke metaphorical source domains) than nouns (Cameron 2003; Deignan 2005; Pragglejaz Group 2007; Steen et al. 2010; Sullivan 2018). Cameron (2003: 88-89) compares the number of metaphors expressed by dif- ferent linguistic forms, which enables her to determine that almost 50 % of metaphors come in the form of verbs. Metaphorical expressions involving nouns (of the type A=B, e.g., Man is a wolf) make up no more than 5 % of the corpus.

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Fig. 1: Williams’ (1976: 463) model of verbal synesthesia.
Fig. 1: Williams’ (1976: 463) model of verbal synesthesia.
Fig. 2: The main window of the ATOS editor. The first part of the MU editor annotated the following attributes of a MU: in the box Fraza tekstowa ‘text phrase, a MU appeared in its actual form. In the box Pena fraza ‘full phrase, annotators could complement missing elements of the phrase in case of ellipsis. Below was the Metafora posrednia ‘indirect met- aphor’ box (more about this problem in the Annotation procedure section and the Metaphors in Synamet chapter). The Temat ‘topic box enabled annotators to choose a topic from the list of topics already marked in the main ATOS window. In the Typ frazy ‘phrase type’ box, annotators could choose a grammat- ical description of the MU from the list (see more about the list in the section Annotation procedure). The Sita MS ‘strength of MU’ box classified the MU as a strong synesthetic metaphor or a weak synesthetic metaphor. The asterisk meant that filling a box was obligatory. The Kategoria ‘category’ box enabled annotators to choose from the list if the MU is a simple synesthesia, a multimodal syn- esthesia, or belongs to the category Inna ‘other’ (when one of the frames was non-perceptual). The last box in the first part of the MU, the editor selected the phrase’s semantic head—Glowa frazy (more about the semantic head in the Annotation procedure section).
Fig. 2: The main window of the ATOS editor. The first part of the MU editor annotated the following attributes of a MU: in the box Fraza tekstowa ‘text phrase, a MU appeared in its actual form. In the box Pena fraza ‘full phrase, annotators could complement missing elements of the phrase in case of ellipsis. Below was the Metafora posrednia ‘indirect met- aphor’ box (more about this problem in the Annotation procedure section and the Metaphors in Synamet chapter). The Temat ‘topic box enabled annotators to choose a topic from the list of topics already marked in the main ATOS window. In the Typ frazy ‘phrase type’ box, annotators could choose a grammat- ical description of the MU from the list (see more about the list in the section Annotation procedure). The Sita MS ‘strength of MU’ box classified the MU as a strong synesthetic metaphor or a weak synesthetic metaphor. The asterisk meant that filling a box was obligatory. The Kategoria ‘category’ box enabled annotators to choose from the list if the MU is a simple synesthesia, a multimodal syn- esthesia, or belongs to the category Inna ‘other’ (when one of the frames was non-perceptual). The last box in the first part of the MU, the editor selected the phrase’s semantic head—Glowa frazy (more about the semantic head in the Annotation procedure section).
Fig. 3: The editor of metaphorical units in ATOS.
Fig. 3: The editor of metaphorical units in ATOS.
Fig. 6: Selection of an activator in the main ATOS’ window. Tab. 2: The list of grammatical descriptions of activators in Synamet.
Fig. 6: Selection of an activator in the main ATOS’ window. Tab. 2: The list of grammatical descriptions of activators in Synamet.
Fig. 10: The initial step of an indirect metaphor annotation in the main window of ATOS.
Fig. 10: The initial step of an indirect metaphor annotation in the main window of ATOS.
Fig. 11: Annotation of a mediator in an indirect metaphor (example—bukiet ‘bouquet).
Fig. 11: Annotation of a mediator in an indirect metaphor (example—bukiet ‘bouquet).
Fig. 12: The last step of an indirect metaphor annotation (konstrukcja bukietu ‘construction of bouquet’).
Fig. 12: The last step of an indirect metaphor annotation (konstrukcja bukietu ‘construction of bouquet’).
Tab. 3: Cohen ‘kappa test for the annotator inter-agreement. Results were as follows (see Tab. 3): a moderate level of agreement for typical metaphors (« = 0.77), an almost perfect level of agreement for mixed metaphors (k = 0.95), a strong level of agreement for entangled metaphors (k = 0.80), and an almost perfect level of agreement for narrative metaphors (k = 0.93). The experi- ment showed that despite the complex procedure of annotation and problematic data, annotator inter-agreement was high enough to expect that the Synamet corpus is relatively trustworthy.
Tab. 3: Cohen ‘kappa test for the annotator inter-agreement. Results were as follows (see Tab. 3): a moderate level of agreement for typical metaphors (« = 0.77), an almost perfect level of agreement for mixed metaphors (k = 0.95), a strong level of agreement for entangled metaphors (k = 0.80), and an almost perfect level of agreement for narrative metaphors (k = 0.93). The experi- ment showed that despite the complex procedure of annotation and problematic data, annotator inter-agreement was high enough to expect that the Synamet corpus is relatively trustworthy.
Tab. 4: Number of blog entries, tokens, and MUs in each category. Tab. 5: Five-number summary of the Synamet categories.
Tab. 4: Number of blog entries, tokens, and MUs in each category. Tab. 5: Five-number summary of the Synamet categories.
Tab. 6: Standardized Pearson residuals of tokens and metaphors in Synamet. The following categories have a number of tokens above expecta- tions: COFFEE, CUISINE, MASSAGE, and WELLNESS. The largest set of syn- esthetic metaphors contains blogs devoted to PERFUME, BEER, MUSIC, and WINE. The remaining categories have a relatively smaller contribution to syn- esthetic metaphors. The Pearson and Kendall correlation tests show no statisti- cally significant correlation between the number of tokens and the number of metaphor units in each category (r = 0.516, t = 1.808, df = 9, p-value = 0.052; t = 0.309, T = 36, p-value = 0.109).
Tab. 6: Standardized Pearson residuals of tokens and metaphors in Synamet. The following categories have a number of tokens above expecta- tions: COFFEE, CUISINE, MASSAGE, and WELLNESS. The largest set of syn- esthetic metaphors contains blogs devoted to PERFUME, BEER, MUSIC, and WINE. The remaining categories have a relatively smaller contribution to syn- esthetic metaphors. The Pearson and Kendall correlation tests show no statisti- cally significant correlation between the number of tokens and the number of metaphor units in each category (r = 0.516, t = 1.808, df = 9, p-value = 0.052; t = 0.309, T = 36, p-value = 0.109).
Tab. 7: The TOUCH frame's structure in Synamet.
Tab. 7: The TOUCH frame's structure in Synamet.
fab. 8: Standardized Pearson residuals of the source and target frames in the strong syn- esthetic metaphors (p-value < 0.0001).
fab. 8: Standardized Pearson residuals of the source and target frames in the strong syn- esthetic metaphors (p-value < 0.0001).
Tab. 9: Raw frequencies of source/target pairs of frames in strong synesthetic metaphors.
Tab. 9: Raw frequencies of source/target pairs of frames in strong synesthetic metaphors.
Tab. 11: Source-and-target frames in the BEER category (p-value < 0.0001).
Tab. 11: Source-and-target frames in the BEER category (p-value < 0.0001).
Tab. 12: Source-and-target frames in the COFFEE category (p-value < 0.0001). Tab. 13: Source-and-target frames in the COSMETICS category (p-value < 0.0001).
Tab. 12: Source-and-target frames in the COFFEE category (p-value < 0.0001). Tab. 13: Source-and-target frames in the COSMETICS category (p-value < 0.0001).
Tab. 14: Source-and-target frames in the CUISINE category (p-value < 0.0001).
Tab. 14: Source-and-target frames in the CUISINE category (p-value < 0.0001).
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Tab. 15: Source-and-target frames in the CULTURE category (p-value < 0.0001). Tab. 16: Source-and-target frames in the MASAGE category.
Tab. 15: Source-and-target frames in the CULTURE category (p-value < 0.0001). Tab. 16: Source-and-target frames in the MASAGE category.
Tab. 17: Source-and-target frames in the MUSIC category (p-value < 0.0001).
Tab. 17: Source-and-target frames in the MUSIC category (p-value < 0.0001).
Tab. 18: Source-and-target frames in the PERFUME category (p-value < 0.0001).
Tab. 18: Source-and-target frames in the PERFUME category (p-value < 0.0001).
Tab. 19: Source-and-target frames in the WELLNESS category. Tab. 20: Source-and-target frames in the WINE category.
Tab. 19: Source-and-target frames in the WELLNESS category. Tab. 20: Source-and-target frames in the WINE category.
Tab. 21: Source-and-target frames in the YERBA category.
Tab. 21: Source-and-target frames in the YERBA category.
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Fig. 17: Pearson residuals of the source frames’ frequencies in Synamet's categories: 1) BEER, 2) COFFEE, 3) COSMETICS, 4) CUISINE, 5) CULTURE, 6) MASSAGE, 7) MUSIC, 8) PERFUME, 9) WELLNESS, 10) WINE, 11) YERBA.
Fig. 17: Pearson residuals of the source frames’ frequencies in Synamet's categories: 1) BEER, 2) COFFEE, 3) COSMETICS, 4) CUISINE, 5) CULTURE, 6) MASSAGE, 7) MUSIC, 8) PERFUME, 9) WELLNESS, 10) WINE, 11) YERBA.
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access Tab. 22: Standardized Pearson residuals for source frames in the text categories in Synamet.
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access Tab. 22: Standardized Pearson residuals for source frames in the text categories in Synamet.
Fig. 18: The most frequent non-perceptual source frames in Synamet.
Fig. 18: The most frequent non-perceptual source frames in Synamet.
fab. 23: Source/target pairs of frames in weak synesthetic metaphors (standardized Pearson residuals, p-value < 0.0001).
fab. 23: Source/target pairs of frames in weak synesthetic metaphors (standardized Pearson residuals, p-value < 0.0001).
Tab. 24: Frequency of non-perceptual frames in BEER, MUSIC, PERFUME, and WINE. Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Tab. 24: Frequency of non-perceptual frames in BEER, MUSIC, PERFUME, and WINE. Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
In the BEER category, the source frame SPORT is characterized by a very high frequency compared to other categories. Moreover, the WILD ANIMAL” source frame is activated more often in the BEER category than in the rest sub-corpora. The MUSIC category exhibits relatively high frequency of source frames: SPACE, OBJECT, WEATHER, JOURNEY, COOKING, PHYSICAL QUANTITY,” BODY OF WATER, and UNIVERSE. In the PERFUME sub- corpus, ARCHITECTURE, EVENT, HOME, PERSON, and TIME are most frequent as source frames. The WINE category exhibits a more frequent acti- vation of the ABSTRACT CONCEPT, PERSON, COOKING, PLANT, and DOMESTIC ANIMAL source frames.
In the BEER category, the source frame SPORT is characterized by a very high frequency compared to other categories. Moreover, the WILD ANIMAL” source frame is activated more often in the BEER category than in the rest sub-corpora. The MUSIC category exhibits relatively high frequency of source frames: SPACE, OBJECT, WEATHER, JOURNEY, COOKING, PHYSICAL QUANTITY,” BODY OF WATER, and UNIVERSE. In the PERFUME sub- corpus, ARCHITECTURE, EVENT, HOME, PERSON, and TIME are most frequent as source frames. The WINE category exhibits a more frequent acti- vation of the ABSTRACT CONCEPT, PERSON, COOKING, PLANT, and DOMESTIC ANIMAL source frames.
‘taste with 119), and DEPTH (MULTIMODAL PERCEPTION frame, e.g., lekki ‘light, with 105) (see Fig. 20).
‘taste with 119), and DEPTH (MULTIMODAL PERCEPTION frame, e.g., lekki ‘light, with 105) (see Fig. 20).
b. 26: Number of evoked elements in the non-perceptua! frames (10 and more elements). Tab. 26: Number of evoked elements in the non-perceptual frames (10 and more elements).
b. 26: Number of evoked elements in the non-perceptua! frames (10 and more elements). Tab. 26: Number of evoked elements in the non-perceptual frames (10 and more elements).
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access Tab. 27: Pairs of elements of the SMELL target frame and the other perceptual frames.
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access Tab. 27: Pairs of elements of the SMELL target frame and the other perceptual frames.
Tab. 28: Pairs of elements of the SMELL frame and elements of the non-perceptual frames.
Tab. 28: Pairs of elements of the SMELL frame and elements of the non-perceptual frames.
Tab. 30: Pairs of elements of the TASTE frame and elements of the non-perceptual frames.
Tab. 30: Pairs of elements of the TASTE frame and elements of the non-perceptual frames.
FEs is activated in the PERFUME category (139 and 386, respectively), a slightly smaller set in MUSIC (108 and 349, respectively), and a much more restricted set in both WINE (81 and 156, respectively), and BEER (58 and 99, respectively). The chi-squared test results are x? = 15.623, df = 3, p-value = 0.001355, Cramer’s V =0.107. The standardized Pearson residuals are presented in Tab. 34. The proportion of perceptual vs. non-perceptual frame element activation is only statistically significant for BEER (predomi- nance of perceptual elements) and MUSIC (predominance of non-perceptual elements) categories. Magdalena Zawisawska - 978-3-631-79331-2
FEs is activated in the PERFUME category (139 and 386, respectively), a slightly smaller set in MUSIC (108 and 349, respectively), and a much more restricted set in both WINE (81 and 156, respectively), and BEER (58 and 99, respectively). The chi-squared test results are x? = 15.623, df = 3, p-value = 0.001355, Cramer’s V =0.107. The standardized Pearson residuals are presented in Tab. 34. The proportion of perceptual vs. non-perceptual frame element activation is only statistically significant for BEER (predomi- nance of perceptual elements) and MUSIC (predominance of non-perceptual elements) categories. Magdalena Zawisawska - 978-3-631-79331-2
fab. 33: Pairs of elements of the VISION target frame and the other perceptual frame elements.
fab. 33: Pairs of elements of the VISION target frame and the other perceptual frame elements.
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access Tab. 35: The HEARING frame elements evoked in texts gathered in the BEER, PERFUME, and WINE categories.
Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access Tab. 35: The HEARING frame elements evoked in texts gathered in the BEER, PERFUME, and WINE categories.
Tab. 36: The MULTIMODAL PERCEPTION frame elements evoked in texts gathered in the BEER, MUSIC, PERFUME, and WINE categories.
Tab. 36: The MULTIMODAL PERCEPTION frame elements evoked in texts gathered in the BEER, MUSIC, PERFUME, and WINE categories.
Tab. 38: The TOUCH frame elements evoked in texts gathered in the BEER, MUSIC, PERFUME, and WINE categories.
Tab. 38: The TOUCH frame elements evoked in texts gathered in the BEER, MUSIC, PERFUME, and WINE categories.
Tab. 39: The VISION frame elements evoked in MUs of the BEER, MUSIC, PERFUME, and WINE categories.
Tab. 39: The VISION frame elements evoked in MUs of the BEER, MUSIC, PERFUME, and WINE categories.
Tab. 40: Elements of non-perceptual frames evoked in the MUs of the BEER category.
Tab. 40: Elements of non-perceptual frames evoked in the MUs of the BEER category.
Tab. 41: Elements of non-perceptual frames evoked in the MUs of the MUSIC category.
Tab. 41: Elements of non-perceptual frames evoked in the MUs of the MUSIC category.
and Gao 2014; Shen 1997; Shen and Cohen 1998; Tsur 1992; Ullmann 1957; Viberg 1984; Williams 1976; Yu 2003). Ullmann (1957) proposes that the transfer goes from so-called lower (touch, taste, smell) to higher (hearing and sight) sen- sory modalities: Touch/Heat > Taste/Scent > Sight + Sound. He observes that touch is the ultimate source of transfers, and sight and hearing are the main targets. According to Ullmann, the prevalence of hearing as the target, over sight, is due to the fact that the lexical field of visual perception is more complex a es a a ee ee ee
and Gao 2014; Shen 1997; Shen and Cohen 1998; Tsur 1992; Ullmann 1957; Viberg 1984; Williams 1976; Yu 2003). Ullmann (1957) proposes that the transfer goes from so-called lower (touch, taste, smell) to higher (hearing and sight) sen- sory modalities: Touch/Heat > Taste/Scent > Sight + Sound. He observes that touch is the ultimate source of transfers, and sight and hearing are the main targets. According to Ullmann, the prevalence of hearing as the target, over sight, is due to the fact that the lexical field of visual perception is more complex a es a a ee ee ee
[ab. 42: Elements of non-perceptual frames evoked in the MUs of the PERFUME category.
[ab. 42: Elements of non-perceptual frames evoked in the MUs of the PERFUME category.
Tab. 42: Continued and contains more lexical items: “Of the two sensory domains at the top end of the scale, sound stands more in need of external support than light, form, or colour; hence the greater frequency of the intrusion of outside elements into the description of acoustic phenomena” (Ullmann 1957: 283). Although the analysis of many scholars essentially proves Ullmann’s claim about the universality of lower-to-higher modality transfer (Shen 1997; Shen and Cohen 1998; Tsur 1992; Viberg 1984; Williams 1976), Viberg’s model of verbal synesthesia is opposite to Ullmann’. Viberg (1984; 1993) proposes that transfer is as follows: SEE > HEAR => FEEL —> TASTE, SMELL. Strik Lievers (2015) tries to find an expla- nation for this obvious contradiction—she claims that, even though Ullmann’s and Viberg’s models differ, they “relate to the same extra-linguistic factors. Underlying both hierarchies is the dominance, in human perception, of sight (and secondarily hearing) over the other sensory modalities” (86). Likewise, Strik Lievers’ (2015: 81) corpus-based research in English and Italian on percep- tual lexicon (nouns, verbs, and adjectives) supports Ullmann’s hypothesis—she notes that touch is the most frequent source (49.3 % in English and 55.6 % in Italian of the analyzed instances) and both hearing and sight are the most fre- quent targets (hearing is the target 52.3 % of the time in English and 50.2 % in Italian, and sight is the target 28 % of the time in English and 42.5 % in Italian of the analyzed instances). She notes that hearing is the sense that attracts the most transfers, from senses both lower and higher in the hierarchy (similar to Ullmann’s model). Strik Lievers (2015) hypothesizes:
Tab. 42: Continued and contains more lexical items: “Of the two sensory domains at the top end of the scale, sound stands more in need of external support than light, form, or colour; hence the greater frequency of the intrusion of outside elements into the description of acoustic phenomena” (Ullmann 1957: 283). Although the analysis of many scholars essentially proves Ullmann’s claim about the universality of lower-to-higher modality transfer (Shen 1997; Shen and Cohen 1998; Tsur 1992; Viberg 1984; Williams 1976), Viberg’s model of verbal synesthesia is opposite to Ullmann’. Viberg (1984; 1993) proposes that transfer is as follows: SEE > HEAR => FEEL —> TASTE, SMELL. Strik Lievers (2015) tries to find an expla- nation for this obvious contradiction—she claims that, even though Ullmann’s and Viberg’s models differ, they “relate to the same extra-linguistic factors. Underlying both hierarchies is the dominance, in human perception, of sight (and secondarily hearing) over the other sensory modalities” (86). Likewise, Strik Lievers’ (2015: 81) corpus-based research in English and Italian on percep- tual lexicon (nouns, verbs, and adjectives) supports Ullmann’s hypothesis—she notes that touch is the most frequent source (49.3 % in English and 55.6 % in Italian of the analyzed instances) and both hearing and sight are the most fre- quent targets (hearing is the target 52.3 % of the time in English and 50.2 % in Italian, and sight is the target 28 % of the time in English and 42.5 % in Italian of the analyzed instances). She notes that hearing is the sense that attracts the most transfers, from senses both lower and higher in the hierarchy (similar to Ullmann’s model). Strik Lievers (2015) hypothesizes:
Tab. 43: Elements of non-perceptual frames evoked in the MUs of the WINE category. The prominence of sight and hearing in human perception is also at the root of the higher reliability that tends to be attributed to these modalities, as reflected in language. These observations have two consequences for synaesthesia. Firstly, it is more ‘natural’ for sight and hearing to be targets than sources. Sources, in the typical adjective-noun synaesthesia, have the function of qualifying and, often, evaluating targets, and it is therefore understandable that they have a preferential connection with the lower, sub- jective and evaluative, sensory modalities. Conversely, targets are more likely to be asso- ciated with the higher, objective, modalities. (88)
Tab. 43: Elements of non-perceptual frames evoked in the MUs of the WINE category. The prominence of sight and hearing in human perception is also at the root of the higher reliability that tends to be attributed to these modalities, as reflected in language. These observations have two consequences for synaesthesia. Firstly, it is more ‘natural’ for sight and hearing to be targets than sources. Sources, in the typical adjective-noun synaesthesia, have the function of qualifying and, often, evaluating targets, and it is therefore understandable that they have a preferential connection with the lower, sub- jective and evaluative, sensory modalities. Conversely, targets are more likely to be asso- ciated with the higher, objective, modalities. (88)
Tab. 45: Analogical elements in the HEARING and SMELL frames.
Tab. 45: Analogical elements in the HEARING and SMELL frames.
Fig. 24: Lexical items evoking perceptual frames in Synamet.
Fig. 24: Lexical items evoking perceptual frames in Synamet.
Fig. 26: The non-perceptual frames with the biggest sets of lexemes.
Fig. 26: The non-perceptual frames with the biggest sets of lexemes.
Tab. 50: Keywords that most frequently evoke the HEARING frame.
Tab. 50: Keywords that most frequently evoke the HEARING frame.
fab. 51: Keywords that most frequently evoke the MULTIMODAL PERCEPTION frame.
fab. 51: Keywords that most frequently evoke the MULTIMODAL PERCEPTION frame.
Tab. 52: Keywords that most frequently evoke the SMELL frame. Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Tab. 52: Keywords that most frequently evoke the SMELL frame. Magdalena Zawisawska - 978-3-631-79331-2 Downloaded from PubFactory at 08/11/2021 02:43:35PM via free access
Tab. 53: Keywords that most frequently evoke the TASTE frame. The biggest set of proper names is linked to the SMELL frame; a fewer proper names are linked to the HEARING and TASTE frames. MULTIMODAL PERCEPTION, TOUCH, and VISION frames are characterized by the biggest set of deadjectival nouns. Deverbal nouns most frequently evoke non-perceptual frames and the TOUCH frame. Common nouns most often activate non-perceptual frames. The Pearson correlation coefficient (r) and Kendall rank correlation coefficient (tau) show some significant results. There is a significant positive correlation between the number of proper names and the number of target frames: r WeeB fact, 5 12.625) df =A 4o,poyalne ;9;0001;
Tab. 53: Keywords that most frequently evoke the TASTE frame. The biggest set of proper names is linked to the SMELL frame; a fewer proper names are linked to the HEARING and TASTE frames. MULTIMODAL PERCEPTION, TOUCH, and VISION frames are characterized by the biggest set of deadjectival nouns. Deverbal nouns most frequently evoke non-perceptual frames and the TOUCH frame. Common nouns most often activate non-perceptual frames. The Pearson correlation coefficient (r) and Kendall rank correlation coefficient (tau) show some significant results. There is a significant positive correlation between the number of proper names and the number of target frames: r WeeB fact, 5 12.625) df =A 4o,poyalne ;9;0001;
Tab. 54: Keywords that most frequently evoke the TOUCH frame.
Tab. 54: Keywords that most frequently evoke the TOUCH frame.
Tab. 55: Keywords that most frequently evoke the VISION frame.
Tab. 55: Keywords that most frequently evoke the VISION frame.
Deignan (2005: 178) restricts her research on the ways metaphors are expressed to lexemes involving the domain of plants; more precisely, she focuses on the English word blossom, which may either be a collective noun denoting ‘flowers’ or a verb meaning ‘to produce flower(s). As she reports, the noun was used 167 times for the literal meaning and only twice in the figurative sense; the verb was used five times in its literal meaning and as many as 55 times for the figurative meaning. Furthermore, Sullivan argues that:
Deignan (2005: 178) restricts her research on the ways metaphors are expressed to lexemes involving the domain of plants; more precisely, she focuses on the English word blossom, which may either be a collective noun denoting ‘flowers’ or a verb meaning ‘to produce flower(s). As she reports, the noun was used 167 times for the literal meaning and only twice in the figurative sense; the verb was used five times in its literal meaning and as many as 55 times for the figurative meaning. Furthermore, Sullivan argues that:
Fig. 27: Grammatical forms of metaphorical units in Synamet.
Fig. 27: Grammatical forms of metaphorical units in Synamet.
Tab. 59: Standardized Pearson residuals of ciemny ‘dark and mroczny ‘dark, obscure’ adjectives in the literal and metaphorical collocates (p-value < 0.0001).
Tab. 59: Standardized Pearson residuals of ciemny ‘dark and mroczny ‘dark, obscure’ adjectives in the literal and metaphorical collocates (p-value < 0.0001).
An analysis of two pairs of synonyms, which are keywords in Synamet, shows that there are several semantic factors that have a considerable impact on metaphorization. First of all, the frame analysis shows that there are differences in their literal senses—especially in the pair ciemny ‘dark and mroczny ‘dark, obscure. The adjective mroczny ‘dark, obscure’ can refer only to a lack of light, while the adjective ciemny ‘dark can also mean weak or dim lighting. According to Sullivan (2006), the metaphor KNOWING IS SEEING can be described as a sys- tematic mapping of the source frame on the target frame (see Tab. 61). Since the adjective ciemny ‘dark can refer to HIGHT SOURCE (e.g., ciemne i: a: co (ee \ |... J TAAtTII|M MAATCOOTISOAVXKT f/f. _ © 4 €3J_..3. Fe. 4igd\ %%
An analysis of two pairs of synonyms, which are keywords in Synamet, shows that there are several semantic factors that have a considerable impact on metaphorization. First of all, the frame analysis shows that there are differences in their literal senses—especially in the pair ciemny ‘dark and mroczny ‘dark, obscure. The adjective mroczny ‘dark, obscure’ can refer only to a lack of light, while the adjective ciemny ‘dark can also mean weak or dim lighting. According to Sullivan (2006), the metaphor KNOWING IS SEEING can be described as a sys- tematic mapping of the source frame on the target frame (see Tab. 61). Since the adjective ciemny ‘dark can refer to HIGHT SOURCE (e.g., ciemne i: a: co (ee \ |... J TAAtTII|M MAATCOOTISOAVXKT f/f. _ © 4 €3J_..3. Fe. 4igd\ %%
Tab. 62: Mapping the source frame onto the target frame in the narrative metaphor of example (36).
Tab. 62: Mapping the source frame onto the target frame in the narrative metaphor of example (36).
Related topics:
Perception
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