Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Duplicate Detection with Efficient Language Models for Automatic
  Bibliographic Heterogeneous Data Integration   We present a new method to detect duplicates used to merge different
bibliographic record corpora with the help of lexical and social information.
As we show, a trivial key is not available to delete useless documents. Merging
heteregeneous document databases to get a maximum of information can be of
interest. In our case we try to build a document corpus about the TOR molecule
so as to extract relationships with other gene components from PubMed and
WebOfScience document databases. Our approach makes key fingerprints based on
n-grams. We made two documents gold standards using this corpus to make an
evaluation. Comparison with other well-known methods in deduplication gives
best scores of recall (95\%) and precision (100\%).

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The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: eess
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics