Towards a W ord N et based Classification of Actors in Folktales (2016)

Abstract: In the context of a student software project we are investigating the use of WordNet for improving the automatic detection and classification of actors (or characters) mentioned in folktales. Our starting point is the book “Classification of International Folktales”, out of which we extract text segments that name the different actors involved in tales, taking advantage of patterns used by its author, Hans-Jo ̈rg Uther. We apply on those text segments functions that are implemented in the NLTK interface to WordNet in order to obtain lexical semantic information to enrich the original naming of characters proposed in the “Classification of International Folktales” and to support their translation in other languages.

## introduction this short paper reports on the current state of a student software project aiming at supporting the automatized classification of folktales along the line of the classification proposed by @xcite . this classification scheme is considered as a central source for the analysis work of folklorists. it builds on former work by antti @xcite and @xcite . in the following, we are using the acronym atu for referring to @xcite : atu standing for aarne-thompson-uther. we focus in the current work on the detection of common superclasses to the naming of the main actors (or characters) that are mentioned in the various types of folktales listed by @xcite . in doing this we are able to propose more generic classes of characters and an extended vocabulary, and so to link to other classification systems, like the motif-index of folk-literature proposed by stith thompson 1 . in general, we are aiming at a wordnet foot_1 based generation of lexical semantic relations for building a terminology network of actors/characters mentioned in folktales. our work is anchored in the field of digital humanities (dh), where there is an increased interest in applying methods from natural language processing (nlp) and semantic web (sw) technologies to literary work. in the following sections we will present first the data we are dealing with and the transformations we applied on those for being able to use the nltk interface to wordnet foot_2 . we describe then the functions of nltk we are using and how we can benefit from those for building a more generic vocabulary and extending the basic terminology for classifying actors/characters in folktales. related work on this topic is presented in declerck (2012), which is more focused on the use of wiktionary for translation and also dealing rather with the formal representation of the terminology used in atu. ## the data source we are taking the atu classification scheme as our starting point. just below we display the initial part of a type of folktale, which in atu is marked using an integer, possibly followed by a letter. in this example we deal with type 2, which is included in the list of types "wild animal" (from type 1 to type 99), and more specifically within the list "the clever fox (other animal)" (from type 1 to type 69) 4 . the tail-fisher. a bear (wolf) meets a fox who has caught a big load of fish. he asks him where he caught them, and the fox replies that he was fishing with his tail through a hole in the ice. he advises the bear to do likewise and the bear does. when the bear tries to pull his tail out of the ice (because men or dogs are attacking him), it is frozen in place. he runs away but leaves his tail behind [k1021]. cf. type 1891. ## conclusion and future work we presented work done in the context of a running student software project consisting in accessing wordnet for providing for lexical semantic information that can be used for enriching an existing classification scheme of folktales with additional terms gained from the extraction of relevant hypernyms (and to a certain extent from hyponyms) of words naming characters playing a central roles in folktales. the aim is to generate a wordnet based network of terms for the folktale domain. as future work, an investigation will be performed in order to determine the optimal length of the path between a lowest common hypernym (lch) and the root node of wordnet as the filtering process for excluding irrelevant and noise introducing lchs. we will also perform an evaluation of the extracted lchs against a manually annotated set of atu entries. and we will compare the french equivalents of the synsets proposed by wordnet with the french terms used in the french wikipedia page for the at. additionally, we plan to compare our wordnet based approach as the basis for the linking between atu and tmi to the machine learning approach to such a linking @xcite .