4,457 research outputs found
Visualizing the Flow of Discourse with a Concept Ontology
Understanding and visualizing human discourse has long being a challenging
task. Although recent work on argument mining have shown success in classifying
the role of various sentences, the task of recognizing concepts and
understanding the ways in which they are discussed remains challenging. Given
an email thread or a transcript of a group discussion, our task is to extract
the relevant concepts and understand how they are referenced and re-referenced
throughout the discussion. In the present work, we present a preliminary
approach for extracting and visualizing group discourse by adapting Wikipedia's
category hierarchy to be an external concept ontology. From a user study, we
found that our method achieved better results than 4 strong alternative
approaches, and we illustrate our visualization method based on the extracted
discourse flows.Comment: 2 pages, accepted to WWW201
Topic Similarity Networks: Visual Analytics for Large Document Sets
We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We describe efficient and effective approaches to both building and labeling
such networks. Visualizations of topic models based on these networks are shown
to be a powerful means of exploring, characterizing, and summarizing large
collections of unstructured text documents. They help to "tease out"
non-obvious connections among different sets of documents and provide insights
into how topics form larger themes. We demonstrate the efficacy and
practicality of these approaches through two case studies: 1) NSF grants for
basic research spanning a 14 year period and 2) the entire English portion of
Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData
2014
The most controversial topics in Wikipedia: A multilingual and geographical analysis
We present, visualize and analyse the similarities and differences between
the controversial topics related to "edit wars" identified in 10 different
language versions of Wikipedia. After a brief review of the related work we
describe the methods developed to locate, measure, and categorize the
controversial topics in the different languages. Visualizations of the degree
of overlap between the top 100 lists of most controversial articles in
different languages and the content related to geographical locations will be
presented. We discuss what the presented analysis and visualizations can tell
us about the multicultural aspects of Wikipedia and practices of
peer-production. Our results indicate that Wikipedia is more than just an
encyclopaedia; it is also a window into convergent and divergent social-spatial
priorities, interests and preferences.Comment: This is a draft of a book chapter to be published in 2014 by
Scarecrow Press. Please cite as: Yasseri T., Spoerri A., Graham M., and
Kert\'esz J., The most controversial topics in Wikipedia: A multilingual and
geographical analysis. In: Fichman P., Hara N., editors, Global
Wikipedia:International and cross-cultural issues in online collaboration.
Scarecrow Press (2014
Ariadne's Thread - Interactive Navigation in a World of Networked Information
This work-in-progress paper introduces an interface for the interactive
visual exploration of the context of queries using the ArticleFirst database, a
product of OCLC. We describe a workflow which allows the user to browse live
entities associated with 65 million articles. In the on-line interface, each
query leads to a specific network representation of the most prevailing
entities: topics (words), authors, journals and Dewey decimal classes linked to
the set of terms in the query. This network represents the context of a query.
Each of the network nodes is clickable: by clicking through, a user traverses a
large space of articles along dimensions of authors, journals, Dewey classes
and words simultaneously. We present different use cases of such an interface.
This paper provides a link between the quest for maps of science and on-going
debates in HCI about the use of interactive information visualisation to
empower users in their search.Comment: CHI'15 Extended Abstracts, April 18-23, 2015, Seoul, Republic of
Korea. ACM 978-1-4503-3146-3/15/0
VMEXT: A Visualization Tool for Mathematical Expression Trees
Mathematical expressions can be represented as a tree consisting of terminal
symbols, such as identifiers or numbers (leaf nodes), and functions or
operators (non-leaf nodes). Expression trees are an important mechanism for
storing and processing mathematical expressions as well as the most frequently
used visualization of the structure of mathematical expressions. Typically,
researchers and practitioners manually visualize expression trees using
general-purpose tools. This approach is laborious, redundant, and error-prone.
Manual visualizations represent a user's notion of what the markup of an
expression should be, but not necessarily what the actual markup is. This paper
presents VMEXT - a free and open source tool to directly visualize expression
trees from parallel MathML. VMEXT simultaneously visualizes the presentation
elements and the semantic structure of mathematical expressions to enable users
to quickly spot deficiencies in the Content MathML markup that does not affect
the presentation of the expression. Identifying such discrepancies previously
required reading the verbose and complex MathML markup. VMEXT also allows one
to visualize similar and identical elements of two expressions. Visualizing
expression similarity can support support developers in designing retrieval
approaches and enable improved interaction concepts for users of mathematical
information retrieval systems. We demonstrate VMEXT's visualizations in two
web-based applications. The first application presents the visualizations
alone. The second application shows a possible integration of the
visualizations in systems for mathematical knowledge management and
mathematical information retrieval. The application converts LaTeX input to
parallel MathML, computes basic similarity measures for mathematical
expressions, and visualizes the results using VMEXT.Comment: 15 pages, 4 figures, Intelligent Computer Mathematics - 10th
International Conference CICM 2017, Edinburgh, UK, July 17-21, 2017,
Proceeding
Wikipedias: Collaborative web-based encyclopedias as complex networks
Wikipedia is a popular web-based encyclopedia edited freely and
collaboratively by its users. In this paper we present an analysis of
Wikipedias in several languages as complex networks. The hyperlinks pointing
from one Wikipedia article to another are treated as directed links while the
articles represent the nodes of the network. We show that many network
characteristics are common to different language versions of Wikipedia, such as
their degree distributions, growth, topology, reciprocity, clustering,
assortativity, path lengths and triad significance profiles. These
regularities, found in the ensemble of Wikipedias in different languages and of
different sizes, point to the existence of a unique growth process. We also
compare Wikipedias to other previously studied networks.Comment: v3: 9 pages, 12 figures, Change of title, few paragraphs and two
figures. Accepted for publication in Phys. Rev.
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