Named Entity detection engine should better deal with hypenated text
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Key: STANBOL-321
URL: https://issues.apache.org/jira/browse/STANBOL-321
Project: Stanbol
Issue Type: Bug
Reporter: Olivier Grisel
Assignee: Olivier Grisel
We need some pre-processing to make it easier for OpenNLP to deal with hyphens,
for instance this is an example of a real PDF:
Sparse RBMs and sparse auto-encoders
(RBM, SAE): In some of our experiments, we
train sparse RBMs (Hinton et al., 2006) and
sparse auto-encoders (Ranzato et al., 2007; Ben-
gio et al., 2006), both using a logistic sigmoid non-
linearity g(W x + b). These algorithms yield a set
of weights W and biases b. To obtain the dictio-
nary, D, we simply discard the biases and take
D = W ⊤ , then normalize the columns of D.
The current implementation return a TextAnnotation with entity-type = Person
for the mention "Ben -" (which is then matched to "Ben Stiller" :P).
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