8,787 research outputs found
Analyzing the Language of Food on Social Media
We investigate the predictive power behind the language of food on social
media. We collect a corpus of over three million food-related posts from
Twitter and demonstrate that many latent population characteristics can be
directly predicted from this data: overweight rate, diabetes rate, political
leaning, and home geographical location of authors. For all tasks, our
language-based models significantly outperform the majority-class baselines.
Performance is further improved with more complex natural language processing,
such as topic modeling. We analyze which textual features have most predictive
power for these datasets, providing insight into the connections between the
language of food, geographic locale, and community characteristics. Lastly, we
design and implement an online system for real-time query and visualization of
the dataset. Visualization tools, such as geo-referenced heatmaps,
semantics-preserving wordclouds and temporal histograms, allow us to discover
more complex, global patterns mirrored in the language of food.Comment: An extended abstract of this paper will appear in IEEE Big Data 201
Common vocabularies for collective intelligence - work in progress
Web based applications and tools offer a great potential to increase the efficiency of information flow and communication among different agents during emergencies. Among the different factors, technical and non technical, that hinder the integration of an information model in emergency management sector, is a lack of a common, shared vocabulary. This paper furthers previous work in the area of ontology development, and presents a summary and overview of the goal, process and methodology to construct a shared set of metadata that can be used to map existing vocabulary. This paper is a work in progress report
Some Ontological Principles for Designing Upper Level Lexical Resources
The purpose of this paper is to explore some semantic problems related to the
use of linguistic ontologies in information systems, and to suggest some
organizing principles aimed to solve such problems. The taxonomic structure of
current ontologies is unfortunately quite complicated and hard to understand,
especially for what concerns the upper levels. I will focus here on the problem
of ISA overloading, which I believe is the main responsible of these
difficulties. To this purpose, I will carefully analyze the ontological nature
of the categories used in current upper-level structures, considering the
necessity of splitting them according to more subtle distinctions or the
opportunity of excluding them because of their limited organizational role.Comment: 8 pages - gzipped postscript file - A4 forma
Semantics and Security Issues in JavaScript
There is a plethora of research articles describing the deep semantics of
JavaScript. Nevertheless, such articles are often difficult to grasp for
readers not familiar with formal semantics. In this report, we propose a digest
of the semantics of JavaScript centered around security concerns. This document
proposes an overview of the JavaScript language and the misleading semantic
points in its design. The first part of the document describes the main
characteristics of the language itself. The second part presents how those
characteristics can lead to problems. It finishes by showing some coding
patterns to avoid certain traps and presents some ECMAScript 5 new features.Comment: Deliverable Resilience FUI 12: 7.3.2.1 Failles de s\'ecurit\'e en
JavaScript / JavaScript security issue
A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams
In the age of Web 2.0, a substantial amount of unstructured
content are distributed through multiple text streams in an
asynchronous fashion, which makes it increasingly difficult
to glean and distill useful information. An effective way to
explore the information in text streams is topic modelling,
which can further facilitate other applications such as search,
information browsing, and pattern mining. In this paper, we
propose a semantic graph based topic modelling approach
for structuring asynchronous text streams. Our model in-
tegrates topic mining and time synchronization, two core
modules for addressing the problem, into a unified model.
Specifically, for handling the lexical gap issues, we use global
semantic graphs of each timestamp for capturing the hid-
den interaction among entities from all the text streams.
For dealing with the sources asynchronism problem, local
semantic graphs are employed to discover similar topics of
different entities that can be potentially separated by time
gaps. Our experiment on two real-world datasets shows that
the proposed model significantly outperforms the existing
ones
Concurrent constraint programming with process mobility
We propose an extension of concurrent constraint programming with primitives for process migration within a hierarchical network, and we study its semantics. To this purpose, we first investigate a "pure " paradigm for process migration, namely a paradigm where the only actions are those dealing with transmissions of processes. Our goal is to give a structural definition of the semantics of migration; namely, we want to describe the behaviour of the system, during the transmission of a process, in terms of the behaviour of the components. We achieve this goal by using a labeled transition system where the effects of sending a process, and requesting a process, are modeled by symmetric rules (similar to handshaking-rules for synchronous communication) between the two partner nodes in the network. Next, we extend our paradigm with the primitives of concurrent constraint programming, and we show how to enrich the semantics to cope with the notions of environment and constraint store. Finally, we show how the operational semantics can be used to define an interpreter for the basic calculus.
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