Text-mining practical

1. Text-mining practical
Lars Juhl Jensen

2. unix primer

3. the command line

4. some useful commands

5. cat

6. less

7. head -10

8. tail -10

9. grep ‘needle’

10. cut -f 2

11. sort

12. sort -nr

13. uniq -c

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

21. the task

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. 62,755 abstracts

33. 65,588 abstracts

34. one directory with each set

35. one file with each abstract

36. dictionary

37. tab-delimited file

38. human genes

39. 22,523 entities

40. synonyms

41. from many databases

42. orthographic variation

43. prefixes and suffixes

44. automatically generated

45. 2,726,495 names

46. tagdir 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

61. a helping hand

62. “black list”

63. 100+ matches

64. 10+ matches

66. wrap up

67. Protein kinase B

68. PKB

69. Akt

70. AKT1

71. same protein

72. synonyms matter

73. “black list” is crucial

74. text mining is useful

75. not black magic

76. Thanks for your attention!
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