Piroska Lendvai

Narrative Language as an Expression of Individual and Group Identity: The Narrative Categorical Content Analysis

Scientific Narrative Psychology integrates quantitative methodologies into the study of identity. Its methodology, Narrative Categorical Analysis, and its toolkit, NarrCat, were both originally developed by the Hungarian Narrative Psychology Group. NarrCat is for machine-made transformation of sentences in self-narratives into psychologically relevant, statistically processable narrative categories. The main body of this flexible and comprehensive system is formed by Psycho-Thematic modules, such as Agency, Evaluation, Emotion, Cognition, Spatiality, and Temporality. The Relational Modules include Social References, Semantic Role Labeling (SRL), and Negation. Certain elements can be combined into Hypermodules, such as Psychological Perspective and Spatio-Temporal Perspective, which allow for even more complex, higher level exploration of composite psychological processes. Using up-to-date developments of corpus linguistics and Natural Language Processing (NLP), a unique feature of NarrCat is its capacity of SRL. The structure of NarrCat, as well as the empirical results in group identity research, is discussed.

Towards Linked Language Data for Digital Humanities

We investigate the extension of classification schemes in the Humanities into semantic data repositories, the benefits of which could be the automation of so far manually conducted processes, such as detecting motifs in folktale texts. In parallel, we propose linguistic analysis of the textual labels used in these repositories. The resulting resource, which we propose to publish in the Linked Open Data (LOD) framework, will explicitly interlink domain knowledge and linguistically enriched language data, which can be used for knowledge-driven content analysis of literary works.

Proppian Content Descriptors in an Integrated Annotation Schema for Fairy Tales

This chapter describes the actual state of APftML (Augmented Proppian fairy tale Markup Language), which is a schema combining linguistic and domain specific annotation for supporting Cultural Heritage and Digital Humanities research, exemplified in the fairy tale domain. APftML should in particular guide automated text analysis to detect and mark up fairy tale characters and the typicalactions they are involved in, which can be subsequently queried in a corpus by both linguists and specialists in the field. The characters and actions are defined with the help of Propp’s formal analysis of folktales, which we aim to implement in a fully fledged way, contrary to existing computational resources based on his theory. Inorder to respond to current formalisation requirements APfML abstracts away from some aspects of the theory of Propp and we also discuss the integration of Proppian elements within modern semantic annotation approaches. The chapter focus on the resulting revised and extended set of narrative elements APftML is dealing with.

Linguistic and semantic representation of the Thompson's Motif-index of folk-literature

We present on-going work on the linguistic and semantic processing of the labels of the Thompsons Motif-Index of Folk-Literature, which has been proposed by Stith Thompson for the classification of narrative elements in folk-literature. We automatically extracted the labels of an on-line version of the Index, and wrote specialised grammars for providing for a multi-layer linguistic annotation of them. We are currently working on enriching the linguistically annotated labels with semantic classes and relations, allowing for a better access to the content of the Index. With this resource, we expect to be able to semiautomatically annotate digitised literary works at the sub-document level by means of automatically comparing the annotated Index with the results of text processing tools applied to those works, and so contribute to a better inter-textual interlinking and understanding of related works in the folkliterature, offering a new way of semantically accessing digital libraries.

Towards a Discourse-driven Taxonomic Inference Model

This chapter describes ongoing work, the goal of which is to create a discourse-driven inference model, as well as to construct resources using such a model. The data process consists of texts from two encyclopedias of the medical domain–stylistic properties characteristic of encyclopedia entries constitute the mechanisms underlying the inference model, such as layout-based features alongside with semantic (conceptual) document structuring. Three parts of the model are explained in detail, providing experimental results that are based on language processing techniques: (i) identifying taxonomic document structure by machine learning; (ii) discourse-driven construction of text–hypothesis pairs for examining types of textual entailment; (iii) semi-supervised harvesting of lexico-semantic patterns that connect medical concept types.

Towards a Formal Representation of Components of German Compounds

This paper presents an approach for the formal representation of compo- nents in German compounds. We as- sume that such a formal representa- tion will support the segmentation and analysis of unseen compounds that feature components already seen in other compounds. An extensive lan- guage resource that explicitly codes components of compounds is Ger- maNet, a lexical semantic network for German. We summarize the Ger- maNet approach to the description of compounds, discussing some of its shortcomings. Our proposed exten- sion of this representation builds on the lemon lexicon model for ontolo- gies, established by the W3C Ontol- ogy Lexicon Community Group.