Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Axiom Injection and Knowledge Base Completion for Efficient
  Natural Language Inference   In logic-based approaches to reasoning tasks such as Recognizing Textual
Entailment (RTE), it is important for a system to have a large amount of
knowledge data. However, there is a tradeoff between adding more knowledge data
for improved RTE performance and maintaining an efficient RTE system, as such a
big database is problematic in terms of the memory usage and computational
complexity. In this work, we show the processing time of a state-of-the-art
logic-based RTE system can be significantly reduced by replacing its
search-based axiom injection (abduction) mechanism by that based on Knowledge
Base Completion (KBC). We integrate this mechanism in a Coq plugin that
provides a proof automation tactic for natural language inference.
Additionally, we show empirically that adding new knowledge data contributes to
better RTE performance while not harming the processing speed in this
framework.

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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con