Table 1: Generic logics according to Schaeffer.
Table 2: Types of summarizing subgenre labels.
Table 3: Top most frequent explicit subgenre labels in the bibliography.
Table 4: Top most frequent subgenres in the bibliography.
Table 5: Set of subgenres used as a basis for the interpretation of implicit signals.
Table 6: Top most frequent thematic subgenre labels in the bibliography.
Table 7: Frequencies of subgenre labels related to literary currents in the bibliography.
Table 8: Set of subgenres used as a basis for the interpretation of literary-historical subgenre labels.
Table 9: Literary-historical sources for the assignment of subgenres.
Table 10: Set of subgenres occurring explicitly or implicitly in the bibliography.
Table 11: Steps for the preparation of structured full text.
Table 12: Error words mapped with general lists of proper nouns.
Table 13: Regular expressions for verb forms with pronoun suffixes.
Table 14: Error words mapped with word patterns.
Table 15: Error words mapped with manually edited exception lists.
Table 16: Values for the time period covered by a novel.
Table 17: Encoding of textual phenomena in the main body of the novels.
Table 18: Encoding of types of written texts represented in the novels.
Table 19: Additional keyword terms for subgenre signals in the text corpus.
Table 20: Elements of the corpus published on GitHub.
Table 21: Authors with most novels in BibACMé and Conha19.
Table 22: Authors with most editions in BibACMé and Conha19.
Table 23: Ranks of discursive levels of subgenre labels, explicit versus literary-historical (Bib-ACMé).
Table 24: Top combinations of thematic subgenre labels.
Table 25: Top combinations of subgenre labels related to the mode of representation.
Table 26: Parameters for general feature sets.
Table 27: Definition and examples of character n-gram subtypes.
Table 28: Most frequent tokens.
Table 29: Word count matrix for the first sentence of the novel “Amalia” (1855, AR) by José Mármol.
Table 30: Word count matrix with word lemmas.
Table 31: Top 15 words of two example topics.
Table 32: Example documentsOut from a topic model.
Table 33: Parameters for topic feature sets.
Table 34: Parameters for classifiers.
Table 35: Experiments for parameter evaluation.
Table 36: Classification results for primary thematic subgenres (topics).
Table 37: Classification results for primary thematic subgenres (SVM, 90 topics, optimization interval of 250).
Table 38: Classification results for primary thematic subgenres (MFW).
Table 39: Classification results for primary thematic subgenres (RF, 3,000 MFW, tf-idf).
Table 40: Classification results for primary literary currents (topics).
Table 41: Classification results for primary literary currents (SVM, 90 topics, optimization interval of 2,500).
Table 42: Classification results for primary literary currents (MFW).
Table 43: Classification results for primary literary currents (SVM, 3,000 MFW, tf-idf).
Table 44: Overview of the family resemblance networks produced.
Table 45: Nearest neighbors in cluster 3 of the network HIST.
Table 46: Sources of the novels in the corpus.
