Text mining

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Text mining techniques can be used to extract information and insights from the exponential growth of scientific literature. Key techniques include information retrieval to find relevant papers, named entity recognition to identify concepts, and information extraction to formalize facts. These techniques can be evaluated using benchmarking against manually annotated corpora, though creating such resources requires significant effort and the pragmatic approach of inspecting text mining outputs is much less work.

Science

Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1
and this modification served as a priming
step to promote subsequent Cdc5-
dependent Swe1 hyperphosphorylation
and degradation

Pafilis et al., Database, 2016extract.hcmr.gr

extract.hcmr.gr Pafilis et al., Database, 2016

what you learned in school
pronoun pronoun verb preposition noun

Gene and protein names
Cue words for entity
recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome
genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]
Saric et al., Proceedings of ACL, 2004

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Text mining

1. Text mining Lars Juhl Jensen

2. >10 km

3. too much to read

4. exponential growth

5. ~40 seconds per paper

6. computer

7. as smart as a dog

8. teach it specific tricks

10.

11. learning objectives

12. information retrieval

13. named entity recognition

14. information extraction

15. benchmarking

16. information retrieval

17. find the relevant papers

18. ad hoc retrieval

19. user-specified query

20. “yeast AND cell cycle”

21. PubMed

22.

23. indexing

24. fast lookup

25. stemming

26. word endings

27. dynamic query expansion

28. MeSH terms

29. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

30. no tool will find that

31. named entity recognition

32. identify the concepts

33. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

34. comprehensive lexicon

35. genes/proteins

36. CDC2

37. cyclin dependent kinase 1

38. chemical compounds

39. organisms

40. diseases

41. orthographic variation

42. flexible matching

43. upper- and lower-case

44. CDC2

45. Cdc2

46. spaces and hyphens

47. cyclin dependent kinase 1

48. cyclin-dependent kinase 1

49. expansion rules

50. prefixes and suffixes

51. CDC2

52. hCDC2

53. abbreviated forms

54. Saccharomyces cerevisiae

55. S. cerevisiae

56. plural/adjective forms

57. mitochondrion

58. mitochondria

59. “black list”

60. SDS

61. augmented browsing

62. EXTRACT

63. interactive annotation

64. Pafilis et al., Database, 2016extract.hcmr.gr

65. extract.hcmr.gr Pafilis et al., Database, 2016

66. information extraction

67. formalize the facts

68. Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5- dependent Swe1 hyperphosphorylation and degradation

69. two approaches

70. the formal way

71. NLP Natural Language Processing

72. part-of-speech tagging

73. what you learned in school pronoun pronoun verb preposition noun

74. sentence parsing

75. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1] Saric et al., Proceedings of ACL, 2004

76. association type

77. direction

78. high precision

79. poor recall

80. domain-specific

81. the pragmatic way

82. guilt by association

83.

84. co-mentioning

85. counting

86. within documents

87. within paragraphs