Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A random forest system combination approach for error detection in
  digital dictionaries   When digitizing a print bilingual dictionary, whether via optical character
recognition or manual entry, it is inevitable that errors are introduced into
the electronic version that is created. We investigate automating the process
of detecting errors in an XML representation of a digitized print dictionary
using a hybrid approach that combines rule-based, feature-based, and language
model-based methods. We investigate combining methods and show that using
random forests is a promising approach. We find that in isolation, unsupervised
methods rival the performance of supervised methods. Random forests typically
require training data so we investigate how we can apply random forests to
combine individual base methods that are themselves unsupervised without
requiring large amounts of training data. Experiments reveal empirically that a
relatively small amount of data is sufficient and can potentially be further
reduced through specific selection criteria.

4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn