Geometric Deep Learning Models for Linking Character Names in Novels (2020)

Abstract: The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.

## introduction characters are mentioned in novels in various ways. they are referred by their first names, surnames, job titles, nick names, diminutives, honorifics and other forms that are ambiguous to some extent. this paper investigates the impact of using geometric deep learning for the purpose of linking names that refer to the same character. geometric deep learning is a set of deep learning methods developed for non-euclidean domains @xcite . in this preliminary study we analyze the impact of using graph convolutional networks @xcite and an edge based training objective on the performance of the character name linking system built upon a non-euclidean domain of a character mentions graph. the problem of linking all names that refer to the same character is a subject of multiple publications. @xcite perform name linking with a rule-based method as a preliminary step for attributing passages of quoted speech in narrative. the same method of linking is used by @xcite for their work on modeling character types. coll ardanuy and sporleder (2014) present another deterministic algorithm for character name resolution which is an integral part of their method of constructing social networks from literary artworks. @xcite propose a multistage pipeline for character detection that transforms the graph of character mentions by adding and removing edges between nodes that should refer to the same character. a more general problem of character identification which encompasses both name and pronoun resolution is discussed in the context of tv shows by @xcite and @xcite . a similar problem of character reference detection is addressed by @xcite for german historic novels. a closely related task of coreference resolution was successfully approached with various deep learning models in recent years @xcite @xcite . ## dataset for this study we use lalka (english: the doll) a polish novel by b. prus for the purpose of training and evaluation. all names that indicate the same character are annotated with a common character identifier which is unique within the scope of the book. the dataset is divided into training and test set that are roughly equal in size and consist of two consecutive sets of chapters. although limited to one novel our corpus with its 150000 tokens and 192 unique characters is comparable in size to the dataset used by choi and @xcite . for the purpose of constructing geometric models the dataset is transformed into an adequate graphical representation of the problem. the conversion procedure has access to the entire text of the corpus and to the character identifiers of mentions that appear in the training set. we use all names that are mentioned in the text as a set of nodes. two mentions l and r are connected by an edge, if one of the following cases holds: ## models we consider two training objectives with respect to the mentions graph that we refer to as node and edge in the rest of the paper. the first one is to predict directly the correct character identifier for every node in the graph. this objective does not require any post-processing steps, however its main disadvantage is the inability to predict identifiers for characters that are not present in the training set. the second objective is to predict the equivalence relation that holds if and only if two nodes in the mentions graph refer to the same character. this objective can be used to predict identifiers for characters that do not appear in the training set, but it requires two additional steps in order to obtain character identifiers for the nodes. first, one has to extract connected components from the graph induced by the predicted relation. second, the character identifiers have to be determined on the basis of the mentions that belong to the same component. 1 figure 1 presents neural network architectures that are set up for the purpose of character name linking. all of them accept at the input vector representations of nodes that belong to the mentions graph. a single node is represented by a vector that encodes the words that belong to a mention (word vec) and a vector that encodes its surrounding context (ctx vec). architectures (a) and (b) are designed for the node objective. they consist of a set of consecutive layers that transform node representations. the number of neurons in the last layer is equal to the number of characters in the novel. the cross-entropy loss is used for the purpose of training. architecture (a) transforms node representations using conventional fully connected layers of neurons (linear in figure 1 ). it depends solely on the vector representations of nodes without taking into consideration the topology of the mentions graph. we confront this architecture with (b) where fully connected layers are replaced with dedicated graph convolutional network layers @xcite that utilise the edge index. this allows us to verify if there is any advantage in using structural information for the task. for the edge objective we develop architectures that transform node representations of (a) and (b) into representations of edges. for this purpose the adjacentsum layer is introduced which returns for every edge in the mentions graph the concatenation of the corresponding node representations and their dot product, i.e. ## experiments for the purpose of evaluation we confront the neural architectures discussed in section 3 with two baseline systems: since the graph induction procedure depends on a proper demarcation of mention boundaries we decided to conduct two types of experiments for every model. first, we use name boundaries detected by the named entity recognizer learned from the mentions that appear in the training set. 4 this is a realistic setup that we use to measure the end-to-end performance of the complete name linking system trained solely from the provided dataset without access to any additional resources. second, we repeat the same experiment with name boundaries obtained from the test set. in this scenario the performance of the name linking model can be measured independently from the named entity recognition technique used to identify mention boundaries in the complete system. results are reported separately for the entire set of characters that appear in the test set and for the characters that where already observed in the training set. micro f-score is the main metric used for the purpose of evaluation. macro f-score which is a metric that penalizes the system for bad performance with respect to the rare characters is also reported. let us start the analysis of the results collected in