26,535 research outputs found
Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation
We present a probabilistic model that uses both prosodic and lexical cues for
the automatic segmentation of speech into topically coherent units. We propose
two methods for combining lexical and prosodic information using hidden Markov
models and decision trees. Lexical information is obtained from a speech
recognizer, and prosodic features are extracted automatically from speech
waveforms. We evaluate our approach on the Broadcast News corpus, using the
DARPA-TDT evaluation metrics. Results show that the prosodic model alone is
competitive with word-based segmentation methods. Furthermore, we achieve a
significant reduction in error by combining the prosodic and word-based
knowledge sources.Comment: 27 pages, 8 figure
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
From mobile phone data to the spatial structure of cities
Pervasive infrastructures, such as cell phone networks, enable to capture
large amounts of human behavioral data but also provide information about the
structure of cities and their dynamical properties. In this article, we focus
on these last aspects by studying phone data recorded during 55 days in 31
Spanish metropolitan areas. We first define an urban dilatation index which
measures how the average distance between individuals evolves during the day,
allowing us to highlight different types of city structure. We then focus on
hotspots, the most crowded places in the city. We propose a parameter free
method to detect them and to test the robustness of our results. The number of
these hotspots scales sublinearly with the population size, a result in
agreement with previous theoretical arguments and measures on employment
datasets. We study the lifetime of these hotspots and show in particular that
the hierarchy of permanent ones, which constitute the "heart" of the city, is
very stable whatever the size of the city. The spatial structure of these
hotspots is also of interest and allows us to distinguish different categories
of cities, from monocentric and "segregated" where the spatial distribution is
very dependent on land use, to polycentric where the spatial mixing between
land uses is much more important. These results point towards the possibility
of a new, quantitative classification of cities using high resolution
spatio-temporal data.Comment: 14 pages, 15 figure
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