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Indeed, regarding semantic categories, it is rather difficult to find a standard, even though Table 4.2 Combinations of degrees of abstraction and of animacy some categories such as “humans” and “animals” seem to be quite agreed upon. The

Table 4 Indeed, regarding semantic categories, it is rather difficult to find a standard, even though Table 4.2 Combinations of degrees of abstraction and of animacy some categories such as “humans” and “animals” seem to be quite agreed upon. The

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Despite the inconsistency of the Latin terminology, Latin rhetoricians appear to treat the
Despite the inconsistency of the Latin terminology, Latin rhetoricians appear to treat the
At the semantic level, in a typical comparative construction such as [s4] “Jean est plus Table 2.3 Main comparatives in English and in French and examples of their usage intelligent que Max”, the standard marker establishes a scale between two degrees of the
At the semantic level, in a typical comparative construction such as [s4] “Jean est plus Table 2.3 Main comparatives in English and in French and examples of their usage intelligent que Max”, the standard marker establishes a scale between two degrees of the
Table 2.4 Types of comparisons and corresponding similes (Bredin, 1998, p. 69-73) 2.2.1 Comparisons of Inequality and Similes
Table 2.4 Types of comparisons and corresponding similes (Bredin, 1998, p. 69-73) 2.2.1 Comparisons of Inequality and Similes
Table 2.5 Salient features of “chair”, “armchair” and “boulder” (Goatly, 2011)
Table 2.5 Salient features of “chair”, “armchair” and “boulder” (Goatly, 2011)
Leech (1969), in his analysis of similes and metaphors in poetry, highlights the fact that the A possible schematisation of the simile “That girl is as graceful as a lily” with respect to this metaphor is far less limited than the simile because it concentrates countless possible
Leech (1969), in his analysis of similes and metaphors in poetry, highlights the fact that the A possible schematisation of the simile “That girl is as graceful as a lily” with respect to this metaphor is far less limited than the simile because it concentrates countless possible
their attributes and relations have already been given. If some examples have bee
their attributes and relations have already been given. If some examples have bee
Oliver Twist (Tomita, 2008 a [p. 2-4] & b [p. 4-6])
Oliver Twist (Tomita, 2008 a [p. 2-4] & b [p. 4-6])
The chosen structure clearly makes this corpus more useful for NLP researchers. In addition, a certain bias has been introduced since the elements of the comparison were already indicated and were not asked to be corrected. As a matter of fact, since the algorithm design does not take coordination into account, in some cases, only partial results are found. Similarly, no distinction is made between the various possible simile pabgiay pubes: eel oe he eee deeded eek hee a beas papa The chosen structure clearly makes this corpus more useful for NLP researchers. In addition, a certain bias has been introduced since the elements of the comparison were An annotated sentence from the Amazon Corpus (Niculae and Danescu-Niculescu-Mizil, Example sults are found. Similarly, no distinction is made between the various possible simile structures which are treated exactly the same.
The chosen structure clearly makes this corpus more useful for NLP researchers. In addition, a certain bias has been introduced since the elements of the comparison were already indicated and were not asked to be corrected. As a matter of fact, since the algorithm design does not take coordination into account, in some cases, only partial results are found. Similarly, no distinction is made between the various possible simile pabgiay pubes: eel oe he eee deeded eek hee a beas papa The chosen structure clearly makes this corpus more useful for NLP researchers. In addition, a certain bias has been introduced since the elements of the comparison were An annotated sentence from the Amazon Corpus (Niculae and Danescu-Niculescu-Mizil, Example sults are found. Similarly, no distinction is made between the various possible simile structures which are treated exactly the same.
metaphor in its broadest sense, and, therefore, devotes a rather small space to similes. This
metaphor in its broadest sense, and, therefore, devotes a rather small space to similes. This
Tables 5.1.A & B give an overview of the possible structures that a simile can take Table 5.1A Simile markers used as predicates: Grammatical patterns and examples
Tables 5.1.A & B give an overview of the possible structures that a simile can take Table 5.1A Simile markers used as predicates: Grammatical patterns and examples
Figure 5.1 Possible syntactic structures of similes
Figure 5.1 Possible syntactic structures of similes
For a sentence to be extracted as a simile candidate, it needs to fulfil the following criteria
For a sentence to be extracted as a simile candidate, it needs to fulfil the following criteria
shoots up from an expiring fire.”
shoots up from an expiring fire.”
Example (The tags used are detailed in appendices 1A and 2.II]) seems to lack flexibility in the case no link exists between the marker and the ground. In the
Example (The tags used are detailed in appendices 1A and 2.II]) seems to lack flexibility in the case no link exists between the marker and the ground. In the
Table 5.4 Correlation between each type of constituent, the clues to identify it and its grammatical
Table 5.4 Correlation between each type of constituent, the clues to identify it and its grammatical
TreeTagger (Schmid, 1994), a part-of-speech tagger that relies on decision trees, was used Table 5.5 Size of the corpus of French prose poems for tokenisation, part-of-speech tagging and syntactic chunking. The results obtained were compared on the manually annotated corpus with an improved version of the method soleils couchants” is taken as three distinct simile candidates.
TreeTagger (Schmid, 1994), a part-of-speech tagger that relies on decision trees, was used Table 5.5 Size of the corpus of French prose poems for tokenisation, part-of-speech tagging and syntactic chunking. The results obtained were compared on the manually annotated corpus with an improved version of the method soleils couchants” is taken as three distinct simile candidates.
As grammatical resources could not be used in all cases and do not seem to make a great
As grammatical resources could not be used in all cases and do not seem to make a great
annotation corpora
annotation corpora
Figure 6.4 Example of an annotated sentence in the original Zooniverse interface The main challenges of the design of the annotation tasks were to decide which
Figure 6.4 Example of an annotated sentence in the original Zooniverse interface The main challenges of the design of the annotation tasks were to decide which
Figure 6.5 Starting question of the annotation platform Once a routine question has been asked on the presence or the reliability of existing annotations, each annotator has to answer to the four questions chronologically listed
Figure 6.5 Starting question of the annotation platform Once a routine question has been asked on the presence or the reliability of existing annotations, each annotator has to answer to the four questions chronologically listed
In the case of a comparison, the transcription task concerns each of its elements and the Figure 6.6 Example of an annotated sentence
In the case of a comparison, the transcription task concerns each of its elements and the Figure 6.6 Example of an annotated sentence
Figure 6.7 Example of a transcription task
Figure 6.7 Example of a transcription task
Figure 6.8 Elements associated with each subtype of pseudo-comparison
Figure 6.8 Elements associated with each subtype of pseudo-comparison
Figure 7.1 Distribution of the novels in the British (top) and the French (bottom) corpora per decade from the 1810s to the 1950s
Figure 7.1 Distribution of the novels in the British (top) and the French (bottom) corpora per decade from the 1810s to the 1950s
1810s 1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1920s 1930s 1940s 1950s
1810s 1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s 1900s 1910s 1920s 1930s 1940s 1950s
Table 7.1 The 10 most frequent similes in both corpora
Table 7.1 The 10 most frequent similes in both corpora
Table 7.2 Most frequent triplets in both languages with the degree of fixedness of the vehicle The relationship between descriptions of some body parts and stereotyped images is further
Table 7.2 Most frequent triplets in both languages with the degree of fixedness of the vehicle The relationship between descriptions of some body parts and stereotyped images is further
Table 7.4 Basic colour term depending on the number of colours expressed in the language Several research works, however, have highlighted important biases that put into questior the veracity and the objectivity of Berlin and Kay’s results. The conception of th
Table 7.4 Basic colour term depending on the number of colours expressed in the language Several research works, however, have highlighted important biases that put into questior the veracity and the objectivity of Berlin and Kay’s results. The conception of th
Figure 7.2 Colour term distribution in fully fledged colour similes and noun+CT similes
Figure 7.2 Colour term distribution in fully fledged colour similes and noun+CT similes
As far as non-basic colour terms are concerned, apart from the fact that they are less used Table 7.6 Pattern distribution of the 5 most frequent colour terms used in noun+CT similes AAS Tar aS non-Dasic colour terms are concerned, apart trom the fact that they are less used in similes, there is no correlation between the noun+CT similes and fully fledged colour similes: for the first type of similes, only instances of “rose” (10), “bay” (1), “gold”(5), “silver” (2), “scarlet” (1), “gilt” (1) and “mauve” (2) were found, whereas for the second type, the ground of the simile is generally inflected forms of existing colour terms (“rosy”(17), “scarlet”(4), “bluish” (1), “blond” (1), “silver” (1) and “greeny” (1)). It is worth noting that in both types of similes, the most frequent colour terms refer to the colour pink, surpassing even the frequency of the colour term “pink” itself.
As far as non-basic colour terms are concerned, apart from the fact that they are less used Table 7.6 Pattern distribution of the 5 most frequent colour terms used in noun+CT similes AAS Tar aS non-Dasic colour terms are concerned, apart trom the fact that they are less used in similes, there is no correlation between the noun+CT similes and fully fledged colour similes: for the first type of similes, only instances of “rose” (10), “bay” (1), “gold”(5), “silver” (2), “scarlet” (1), “gilt” (1) and “mauve” (2) were found, whereas for the second type, the ground of the simile is generally inflected forms of existing colour terms (“rosy”(17), “scarlet”(4), “bluish” (1), “blond” (1), “silver” (1) and “greeny” (1)). It is worth noting that in both types of similes, the most frequent colour terms refer to the colour pink, surpassing even the frequency of the colour term “pink” itself.
Figure 7.3 Relative frequency of each colour term per decade
Figure 7.3 Relative frequency of each colour term per decade
Table 7.8 Lexical diversity per colour for both literary periods If all these results tend to confirm the initial hypothesis about the important place of
Table 7.8 Lexical diversity per colour for both literary periods If all these results tend to confirm the initial hypothesis about the important place of
APPENDIX 6 BRITISH AUTHORS IN THE CORPUS
APPENDIX 6 BRITISH AUTHORS IN THE CORPUS
Related topics:
Computer ScienceArtificial IntelligenceNatural Language Processing
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