52,272 research outputs found
Automated construction and analysis of political networks via open government and media sources
We present a tool to generate real world political networks from user provided lists of politicians and news sites. Additional output includes visualizations, interactive tools and maps that allow a user to better understand the politicians and their surrounding environments as portrayed by the media. As a case study, we construct a comprehensive list of current Texas politicians, select news sites that convey a spectrum of political viewpoints covering Texas politics, and examine the results. We propose a ”Combined” co-occurrence distance metric to better reflect the relationship between two entities. A topic modeling technique is also proposed as a novel, automated way of labeling communities that exist within a politician’s ”extended” network.Peer ReviewedPostprint (author's final draft
Exploratory Analysis of Highly Heterogeneous Document Collections
We present an effective multifaceted system for exploratory analysis of
highly heterogeneous document collections. Our system is based on intelligently
tagging individual documents in a purely automated fashion and exploiting these
tags in a powerful faceted browsing framework. Tagging strategies employed
include both unsupervised and supervised approaches based on machine learning
and natural language processing. As one of our key tagging strategies, we
introduce the KERA algorithm (Keyword Extraction for Reports and Articles).
KERA extracts topic-representative terms from individual documents in a purely
unsupervised fashion and is revealed to be significantly more effective than
state-of-the-art methods. Finally, we evaluate our system in its ability to
help users locate documents pertaining to military critical technologies buried
deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery
and Data Minin
Towards Best Practice Standards for Enhanced Knowledge Discovery Systems
Assessing enhanced knowledge discovery systems (eKDSs) constitutes an intricate issue that is understood merely to a certain extent by now. Based upon an analysis of why it is difficult to formally evaluate eKDSs, it is argued for a change of perspective: eKDSs should be understood as intelligent tools for qualitative analysis that support, rather than substitute, the user in the exploration of the data; a qualitative gap will be identified as the main reason why the evaluation of enhanced knowledge discovery systems is difficult. In order to deal with this problem, the construction of a best practice model for eKDSs is advocated. Based on a brief recapitulation of similar work on spoken language dialogue systems, first steps towards achieving this goal are performed, and directions of future research are outlined
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
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes
In this paper, we present a label transfer model from texts to images for
image classification tasks. The problem of image classification is often much
more challenging than text classification. On one hand, labeled text data is
more widely available than the labeled images for classification tasks. On the
other hand, text data tends to have natural semantic interpretability, and they
are often more directly related to class labels. On the contrary, the image
features are not directly related to concepts inherent in class labels. One of
our goals in this paper is to develop a model for revealing the functional
relationships between text and image features as to directly transfer
intermodal and intramodal labels to annotate the images. This is implemented by
learning a transfer function as a bridge to propagate the labels between two
multimodal spaces. However, the intermodal label transfers could be undermined
by blindly transferring the labels of noisy texts to annotate images. To
mitigate this problem, we present an intramodal label transfer process, which
complements the intermodal label transfer by transferring the image labels
instead when relevant text is absent from the source corpus. In addition, we
generalize the inter-modal label transfer to zero-shot learning scenario where
there are only text examples available to label unseen classes of images
without any positive image examples. We evaluate our algorithm on an image
classification task and show the effectiveness with respect to the other
compared algorithms.Comment: The paper has been accepted by IEEE Transactions on Pattern Analysis
and Machine Intelligence. It will apear in a future issu
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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