Text-mining practical

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Lars Juhl Jensen

Text-mining practical

Text-mining practical
Lars Juhl Jensen

unix primer

the command line

some useful commands

cat

less

head -10

tail -10

grep ‘needle’

cut -f 2

sort

sort -nr

uniq -c

redirecting output

write to file

command > filename

using pipes

command1 | command2

putting it all together

cut -f 4 infile | sort | uniq -c |
sort -nr | head -100 > outfile

the task

disease gene finding

named entity recognition

human genes

gene prioritization

what I have done

information retrieval

two diseases

prostate cancer

schizophrenia

two sets of documents

82,373 abstracts

89,904 abstracts

one file with each set

one line per abstract

dictionary

tab-delimited file

human genes

21,929 entities

synonyms

from many databases

orthographic variation

prefixes and suffixes

automatically generated

2,920,042 names

tagcorpus program

flexible matching

upper- and lower-case

spaces and hyphens

tab-delimited output

what you will do

named entity recognition

find unfortunate names

create “black list”

information extraction

co-mentioning

within abstracts

ank genes for each disease

find shared gene

wrap up

Protein kinase B

PKB

Akt

AKT1

same protein

synonyms matter

“black list” is crucial

text mining is useful

not black magic

Thanks for your attention

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Text-mining practical

1. Text-mining practical Lars Juhl Jensen

3. the command line

4. some useful commands

9. grep ‘needle’

14. redirecting output

15. write to file

16. command > filename

17. using pipes

18. command1 | command2

19. putting it all together

20. cut -f 4 infile | sort | uniq -c | sort -nr | head -100 > outfile

22. disease gene finding

23. named entity recognition

24. human genes

25. gene prioritization

26. what I have done

27. information retrieval

28. two diseases

29. prostate cancer

30. schizophrenia

31. two sets of documents

32. 82,373 abstracts

33. 89,904 abstracts

34. one file with each set

35. one line per abstract

37. tab-delimited file

38. human genes

39. 21,929 entities

41. from many databases

42. orthographic variation

43. prefixes and suffixes

44. automatically generated

45. 2,920,042 names

46. tagcorpus program

47. flexible matching

48. upper- and lower-case

49. spaces and hyphens

50. tab-delimited output

51. what you will do

52. named entity recognition

53. find unfortunate names

54. create “black list”

55. information extraction

56. co-mentioning

57. within abstracts

58. ank genes for each disease

59. find shared gene

60.

62. Protein kinase B

66. same protein

67. synonyms matter

68. “black list” is crucial

69. text mining is useful

70. not black magic

71. Thanks for your attention