Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar
  Exam Questions   Getting an overview over the legal domain has become challenging, especially
in a broad, international context. Legal question answering systems have the
potential to alleviate this task by automatically retrieving relevant legal
texts for a specific statement and checking whether the meaning of the
statement can be inferred from the found documents. We investigate a
combination of the BM25 scoring method of Elasticsearch with word embeddings
trained on English translations of the German and Japanese civil law. For this,
we define criteria which select a dynamic number of relevant documents
according to threshold scores. Exploiting two deep learning classifiers and
their respective prediction bias with a threshold-based answer inclusion
criterion has shown to be beneficial for the textual entailment task, when
compared to the baseline.

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The categories of this paper are: alg-geom
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The categories of this paper are: econ
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an