Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extracting Conceptual Knowledge from Natural Language Text Using Maximum
  Likelihood Principle   Domain-specific knowledge graphs constructed from natural language text are
ubiquitous in today's world. In many such scenarios the base text, from which
the knowledge graph is constructed, concerns itself with practical, on-hand,
actual or ground-reality information about the domain. Product documentation in
software engineering domain are one example of such base texts. Other examples
include blogs and texts related to digital artifacts, reports on emerging
markets and business models, patient medical records, etc. Though the above
sources contain a wealth of knowledge about their respective domains, the
conceptual knowledge on which they are based is often missing or unclear.
Access to this conceptual knowledge can enormously increase the utility of
available data and assist in several tasks such as knowledge graph completion,
grounding, querying, etc.
  Our contributions in this paper are twofold. First, we propose a novel
Markovian stochastic model for document generation from conceptual knowledge.
The uniqueness of our approach lies in the fact that the conceptual knowledge
in the writer's mind forms a component of the parameter set of our stochastic
model. Secondly, we solve the inverse problem of learning the best conceptual
knowledge from a given document, by finding model parameters which maximize the
likelihood of generating the specific document over all possible parameter
values. This likelihood maximization is done using an application of Baum-Welch
algorithm, which is a known special case of Expectation-Maximization (EM)
algorithm. We run our conceptualization algorithm on several well-known natural
language sources and obtain very encouraging results. The results of our
extensive experiments concur with the hypothesis that the information contained
in these sources has a well-defined and rigorous underlying conceptual
structure, which can be discovered using our method.

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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: chem-ph