16,251 research outputs found
Computing the Affective-Aesthetic Potential of Literary Texts
In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results
Quantifying origin and character of long-range correlations in narrative texts
In natural language using short sentences is considered efficient for
communication. However, a text composed exclusively of such sentences looks
technical and reads boring. A text composed of long ones, on the other hand,
demands significantly more effort for comprehension. Studying characteristics
of the sentence length variability (SLV) in a large corpus of world-famous
literary texts shows that an appealing and aesthetic optimum appears somewhere
in between and involves selfsimilar, cascade-like alternation of various
lengths sentences. A related quantitative observation is that the power spectra
S(f) of thus characterized SLV universally develop a convincing `1/f^beta'
scaling with the average exponent beta =~ 1/2, close to what has been
identified before in musical compositions or in the brain waves. An
overwhelming majority of the studied texts simply obeys such fractal attributes
but especially spectacular in this respect are hypertext-like, "stream of
consciousness" novels. In addition, they appear to develop structures
characteristic of irreducibly interwoven sets of fractals called multifractals.
Scaling of S(f) in the present context implies existence of the long-range
correlations in texts and appearance of multifractality indicates that they
carry even a nonlinear component. A distinct role of the full stops in inducing
the long-range correlations in texts is evidenced by the fact that the above
quantitative characteristics on the long-range correlations manifest themselves
in variation of the full stops recurrence times along texts, thus in SLV, but
to a much lesser degree in the recurrence times of the most frequent words. In
this latter case the nonlinear correlations, thus multifractality, disappear
even completely for all the texts considered. Treated as one extra word, the
full stops at the same time appear to obey the Zipfian rank-frequency
distribution, however.Comment: 28 pages, 8 figures, accepted for publication in Information Science
Exploring manuscripts: sharing ancient wisdoms across the semantic web
Recent work in digital humanities has seen researchers in-creasingly producing online editions of texts and manuscripts, particularly in adoption of the TEI XML format for online publishing. The benefits of semantic web techniques are un-derexplored in such research, however, with a lack of sharing and communication of research information. The Sharing Ancient Wisdoms (SAWS) project applies linked data prac-tices to enhance and expand on what is possible with these digital text editions. Focussing on Greek and Arabic col-lections of ancient wise sayings, which are often related to each other, we use RDF to annotate and extract seman-tic information from the TEI documents as RDF triples. This allows researchers to explore the conceptual networks that arise from these interconnected sayings. The SAWS project advocates a semantic-web-based methodology, en-hancing rather than replacing current workflow processes, for digital humanities researchers to share their findings and collectively benefit from each other’s work
Leveraging a Narrative Ontology to Query a Literary Text
In this work we propose a model for the representation of the narrative of a literary text. The model is structured in an ontology and a lexicon constituting a knowledge base that can be queried by a system. This narrative ontology, as well as describing the actors, locations, situations found in the text, provides an explicit formal representation of the timeline of the story. We will focus on a specific case study, that of the representation of a selected portion of Homer\u27s Odyssey, in particular of the knowledge required to answer a selection of salient queries, formulated by a literary scholar. This work is being carried out within the framework of the Semantic Web by adopting models and standards such as RDF, OWL, SPARQL, and lemon among others
ArCo: the Italian Cultural Heritage Knowledge Graph
ArCo is the Italian Cultural Heritage knowledge graph, consisting of a
network of seven vocabularies and 169 million triples about 820 thousand
cultural entities. It is distributed jointly with a SPARQL endpoint, a software
for converting catalogue records to RDF, and a rich suite of documentation
material (testing, evaluation, how-to, examples, etc.). ArCo is based on the
official General Catalogue of the Italian Ministry of Cultural Heritage and
Activities (MiBAC) - and its associated encoding regulations - which collects
and validates the catalogue records of (ideally) all Italian Cultural Heritage
properties (excluding libraries and archives), contributed by CH administrators
from all over Italy. We present its structure, design methods and tools, its
growing community, and delineate its importance, quality, and impact
Science in Wonderland
Lewis Carroll's Alice, who first explores Wonderland (1865) and later on the country behind the Looking-Glass (1872), belongs to the most well-known characters in world literature. [...] The scientific reception of Carroll's stories – concerning physics as well as the humanities – has taken place on different levels. On the one hand, […] various Carrollian ideas and episodes obviously correspond to topics, subjects and models that are treated in the contexts of scientific discourses. Therefore, they can be quoted or alluded to in order to represent theories and questions […] – as […] physical models of the world […]or theoretical models of language and communication. […] On a more abstract level of observation, Carroll's stories have been used in order to explain and to discuss the pre-conditions, the procedures, and the limits . of scientific modeling as such. Above all, they make it possible to narrate on the problem of defining and observing an 'object' of research. […] According to Deleuze, the paradox structures of the world that Alice experiences give an idea of all meaning being groundless and all logic being subverted by the illogical. Finally, besides all affinities of Alice's adventures to scientific attempts to explain the world, the absolutely incomprehensible is present in Carroll's books as well. Especially the self proves to be something profoundly incomprehensible […]
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
- …