Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Anatomy of a Modular System for Media Content Analysis   Intelligent systems for the annotation of media content are increasingly
being used for the automation of parts of social science research. In this
domain the problem of integrating various Artificial Intelligence (AI)
algorithms into a single intelligent system arises spontaneously. As part of
our ongoing effort in automating media content analysis for the social
sciences, we have built a modular system by combining multiple AI modules into
a flexible framework in which they can cooperate in complex tasks. Our system
combines data gathering, machine translation, topic classification, extraction
and annotation of entities and social networks, as well as many other tasks
that have been perfected over the past years of AI research. Over the last few
years, it has allowed us to realise a series of scientific studies over a vast
range of applications including comparative studies between news outlets and
media content in different countries, modelling of user preferences, and
monitoring public mood. The framework is flexible and allows the design and
implementation of modular agents, where simple modules cooperate in the
annotation of a large dataset without central coordination.

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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph