2,728 research outputs found
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood
OpenStreetMap offers a valuable source of worldwide geospatial data useful to
urban researchers. This study uses the OSMnx software to automatically download
and analyze 27,000 US street networks from OpenStreetMap at metropolitan,
municipal, and neighborhood scales - namely, every US city and town, census
urbanized area, and Zillow-defined neighborhood. It presents empirical findings
on US urban form and street network characteristics, emphasizing measures
relevant to graph theory, transportation, urban design, and morphology such as
structure, connectedness, density, centrality, and resilience. In the past,
street network data acquisition and processing have been challenging and ad
hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently
conduct street network analysis with extremely large sample sizes, with clearly
defined network definitions and extents for reproducibility, and using
nonplanar, directed graphs. These street networks and measures data have been
shared in a public repository for other researchers to use
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