Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Legal Document Retrieval using Document Vector Embeddings and Deep
  Learning   Domain specific information retrieval process has been a prominent and
ongoing research in the field of natural language processing. Many researchers
have incorporated different techniques to overcome the technical and domain
specificity and provide a mature model for various domains of interest. The
main bottleneck in these studies is the heavy coupling of domain experts, that
makes the entire process to be time consuming and cumbersome. In this study, we
have developed three novel models which are compared against a golden standard
generated via the on line repositories provided, specifically for the legal
domain. The three different models incorporated vector space representations of
the legal domain, where document vector generation was done in two different
mechanisms and as an ensemble of the above two. This study contains the
research being carried out in the process of representing legal case documents
into different vector spaces, whilst incorporating semantic word measures and
natural language processing techniques. The ensemble model built in this study,
shows a significantly higher accuracy level, which indeed proves the need for
incorporation of domain specific semantic similarity measures into the
information retrieval process. This study also shows, the impact of varying
distribution of the word similarity measures, against varying document vector
dimensions, which can lead to improvements in the process of legal information
retrieval.

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The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat