Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Building a Knowledge Graph from Natural Language Definitions for
  Interpretable Text Entailment Recognition   Natural language definitions of terms can serve as a rich source of
knowledge, but structuring them into a comprehensible semantic model is
essential to enable them to be used in semantic interpretation tasks. We
propose a method and provide a set of tools for automatically building a graph
world knowledge base from natural language definitions. Adopting a conceptual
model composed of a set of semantic roles for dictionary definitions, we
trained a classifier for automatically labeling definitions, preparing the data
to be later converted to a graph representation. WordNetGraph, a knowledge
graph built out of noun and verb WordNet definitions according to this
methodology, was successfully used in an interpretable text entailment
recognition approach which uses paths in this graph to provide clear
justifications for entailment decisions.

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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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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: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th