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Text-mining practical
1.
Lars Juhl Jensen Text-mining
practical
2.
the task
3.
named entity recognition
4.
human proteins
5.
link proteins to
diseases
6.
what I have
done
7.
information retrieval
8.
two diseases
9.
prostate cancer
10.
schizophrenia
11.
two sets of
documents
12.
62,755 abstracts
13.
65,588 abstracts
14.
one directory with
each set
15.
one file with
each abstract
16.
dictionary
17.
tab-delimited file
18.
human proteins
19.
22,523 entities
20.
synonyms
21.
from many databases
22.
orthographic variation
23.
prefixes and postfixes
24.
automatically generated
25.
2,726,495 names
26.
tagdir program
27.
flexible matching
28.
upper- and lower-case
29.
spaces and hyphens
30.
tab-delimited output
31.
what you will
do
32.
named entity recognition
33.
find unfortunate names
34.
create “black list”
35.
information extraction
36.
co-mentioning
37.
within documents
38.
link proteins to
diseases
39.
link between the
diseases
40.
41.
a helping hand
42.
“black list”
43.
100+ matches
44.
10+ matches
45.
46.
wrap up
47.
prostate cancer
48.
FOLH1
49.
schizophrenia
50.
Glutamate carboxypeptidase II
51.
same protein
52.
synonyms matter
53.
“black list” is
crucial
54.
text mining is
useful
55.
not black magic
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