Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Denoising Distantly Supervised Named Entity Recognition via a
  Hypergeometric Probabilistic Model   Denoising is the essential step for distant supervision based named entity
recognition. Previous denoising methods are mostly based on instance-level
confidence statistics, which ignore the variety of the underlying noise
distribution on different datasets and entity types. This makes them difficult
to be adapted to high noise rate settings. In this paper, we propose
Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised
NER that takes both noise distribution and instance-level confidence into
consideration. Specifically, during neural network training, we naturally model
the noise samples in each batch following a hypergeometric distribution
parameterized by the noise-rate. Then each instance in the batch is regarded as
either correct or noisy one according to its label confidence derived from
previous training step, as well as the noise distribution in this sampled
batch. Experiments show that HGL can effectively denoise the weakly-labeled
data retrieved from distant supervision, and therefore results in significant
improvements on the trained models.

10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg