To determine the latest characters said regarding fantasy statement, i first built a database off nouns writing about the three version of stars considered from the Hall–Van de- Palace system: somebody, dogs and you may fictional characters.
person with the words that are subclass of or instance of the item Person in Wikidata blk. Similarly, for animal names, we merged all the words under the noun.animal lexical domain of Wordnet with the words that are subclass of or instance of the item Animal in Wikidata. To identify fictional characters, we considered the words that are subclass of or instance of the Wikidata items Fictional Human, Mythical Creature and Fictional Creature. As a result, we obtained three disjoint sets containing nouns describing people NSomeone (25 850 words), animals NPets (1521 words) and fictional characters NFictional (515 words). These three sets contain both common nouns (e.g. fox, waiter) and proper nouns (e.g. Jack, Gandalf). Dry and fictional characters are grouped into a set of Imaginary characters (CImaginary).
Having those three sets, the tool is able to extract characters from the dream report. It does so by intersecting these three sets with the set of all the proper and common nouns contained in the report (NFantasy). In so doing, the tool extracts the full set of characters C = C People ? C Animals ? C Fictional , where C People = N Dream ? N People is the the set of person characters, C Animals = N Dream ? N Animals is the set of animal characters, and C Fictional = N Dream ? N Fictional is the set of fictional characters. Note that the tool does not use pronouns to identify characters because: (i) the dreamer (most often referred to as ‘I’ in the reports) is not considered as a character in the Hall–Van de Castle guidelines; and (ii) our assumption is that dream reports are self-contained, in that, all characters are introduced with a common or proper name.
4.step 3.step 3. Features from characters
In line with the official guidelines for dream coding, the tool identifies the sex of people characters only, and it does so as follows. If the character is introduced with a common name, the tool searches the character (noun) on Wikidata for the property sex or gender. In so doing, the tool builds two additional sets from the dream report: the set of male characters CGuys, and that of female characters CWomen.
To have the product to be able to identify dry letters (just who function the fresh new selection of imaginary emails because of the in earlier times identified fictional characters), i built-up a first listing of passing-related words taken from the first guidance [16,26] (e.grams. deceased, pass away, corpse), and you can manually expanded one number that have synonyms away from thesaurus to improve coverage, and that kept us which have a last list of 20 terminology.
Rather, in case the character was produced which have a proper identity, the unit suits the character which have a custom set of thirty-two 055 names whoever intercourse known-since it is commonly carried out in gender studies one to manage unstructured text message data on the internet [74,75]
The tool then matches these terms with all the nodes in the dream report’s tree. For each matching node (i.e. for each death-related word), the tool computes the distance between that node and each of the other nodes previously identified as ‘characters’. The tool marks the character at the closest distance as ‘dead’ and adds it to the set of dead characters CDead. The distance between any two nodes u and v in the tree is calculated with the standard formula: