The document discusses methods for measuring semantic similarity and relatedness within the biomedical domain, supported by various sources of information such as ontologies, corpora, and definitions. It covers applications like word sense disambiguation and sentiment classification, and introduces several established measures and tools including WordNet and UMLS. Key findings highlight that semantic similarity can be quantified through different methodologies, and a variety of open-source software is available to aid in these assessments.

1. Measuring Semantic Similarity and Relatedness in the Biomedical Domain : Methods and Applications Ted Pedersen, Ph.D. Department of Computer Science University of Minnesota, Duluth [email_address] http://www.d.umn.edu/~tpederse February 21, 2012

2. Acknowledgments This work on semantic similarity and relatedness has been supported by a National Science Foundation CAREER award (2001 – 2007, #0092784, PI Pedersen) and by the National Library of Medicine, National Institutes of Health (2008 – present, 1R01LM009623-01A2, PI Pakhomov)

3. The contents of this talk are solely my responsibility and do not necessarily represent the official views of the National Science Foundation or the National Institutes of Health.

4. Topics Semantic similarity vs. semantic relatedness

5. How to measure similarity With ontologies and corpora How to measure relatedness With definitions and corpora Applications? Word Sense Disambiguation

6. Sentiment Classification

7. What are we measuring? Concept pairs Assign a numeric value that quantifies how similar or related two concepts are Not words Must know concept underlying a word form

8. Cold may be temperature or illness Concept Mapping

9. Word Sense Disambiguation But, WSD can be done using semantic similarity! SenseRelate, later in the talk

10. Why? Being able to organize concepts by their similarity or relatedness to each other is a fundamental operation in the human mind, and in many problems in Natural Language Processing and Artificial Intelligence

11. If we know a lot about X, and if we know Y is similar to X, then a lot of what we know about X may apply to Y Use X to explain or categorize Y

12. GOOD NEWS! Free Open Source Software! WordNet::Similarity http://wn-similarity.sourceforge.net

13. General English

14. Widely used (+550 citations) UMLS::Similarity http://umls-similarity.sourceforge.net

15. Unified Medical Language System

16. Spun off from WordNet::Similarity But has added a whole lot!

17. Similar or Related? Similarity based on is-a relations How much is X like Y?

18. Share ancestor in is-a hierarchy LCS : least common subsumer

19. Closer / deeper the ancestor the more similar Tetanus and strep throat are similar both are kinds-of bacterial infections

20. Least Common Subsumer (LCS)

21. Similar or Related ? Relatedness more general How much is X related to Y?

22. Many ways to be related is-a, part-of, treats, affects, symptom-of, ... Tetanus and deep cuts are related but they really aren't similar (deep cuts can cause tetanus) All similar concepts are related, but not all related concepts are similar

23. Measures of Similarity (WordNet::Similarity & UMLS::Similarity ) Path Based Rada et al., 1989 (path)

24. Caviedes & Cimino, 2004 (cdist)* cdist only in UMLS::Similarity Path + Depth Wu & Palmer, 1994 (wup)

25. Leacock & Chodorow, 1998 (lch) Zhong et al., 2002 (zhong)* Nguyen & Al-Mubaid, 2006 (nam)* zhong and nam only in UMLS::Similarity

26. Measures of Similarity (WordNet::Similarity & UMLS::Similarity) Path + Information Content Resnik, 1995 (res)

27. Jiang & Conrath, 1997 (jcn)

28. Lin, 1998 (lin)

29. Path Based Measures Distance between concepts (nodes) in tree intuitively appealing

30. Spatial orientation, good for networks or maps but not is-a hierarchies Reasonable approximation sometimes

31. Assumes all paths have same “weight”

32. But, more specific (deeper) paths tend to travel less semantic distance Shortest path a good start, but needs corrections

33. Shortest is-a Path 1 path(a,b) = ------------------------------

34. shortest is-a path(a,b)

35. We count nodes... Maximum = 1 self similarity

36. path(tetanus,tetanus) = 1 Minimum = 1 / (longest path in isa tree) path(typhoid, oral thrush) = 1/7

37. path(food poisoning, strep throat) = 1/7

38. etc...

39. path(strep throat, tetanus) = .25

40. path (bacterial infections, yeast infections) = .25

41. ? Are bacterial infections and yeast infections similar to the same degree as are tetanus and strep throat ?

42. The path measure says “yes, they are.”

43. Path + Depth Path only doesn't account for specificity

44. Deeper concepts more specific

45. Paths between deeper concepts travel less semantic distance

46. Wu and Palmer, 1994 2 * depth (LCS (a,b)) wup(a,b) = ----------------------------

47. depth (a) + depth (b)

48. depth(x) = shortest is-a path(root,x)

49. wup(strep throat, tetanus) = (2*2)/(4+3) = .57

50. wup (bacterial infections, yeast infections) = (2*1)/(2+3) = .4

51. ? Wu and Palmer say that strep throat and tetanus (.57) are more similar than are bacterial infections and yeast infections (.4)

52. Path says that strep throat and tetanus (.25) are equally similar as are bacterial infections and yeast infections (.25)

53. Information Content ic(concept) = -log p(concept) [Resnik 1995] Need to count concepts

54. Term frequency +Inherited frequency

55. p(concept) = tf + if / N Depth shows specificity but not frequency

56. Low frequency concepts often much more specific than high frequency ones

57. Information Content term frequency (tf)

58. Information Content inherited frequency (if)

59. Information Content (IC = -log (f/N)) final count (f = tf + if, N = 365,820)

60. Lin, 1998 2 * IC (LCS (a,b)) lin(a,b) = --------------------------

61. IC (a) + IC (b)

62. Look familiar?

63. 2* depth (LCS (a,b) )

64. wup(a,b) = ------------------------------

65. depth(a) + depth (b)

66. lin (strep throat, tetanus) = 2 * 2.26 / (5.21 + 4.11) = 0.485

67. lin (strep throat, tetanus) = 2 * 2.26 / (5.21 + 4.11) = 0.485

68. lin (bacterial infection, yeast infection) = 2 * 0.71 / (2.26+2.81) = 0.280

69. ? Lin says that strep throat and tetanus (.49) are more similar than are bacterial infection and yeast infection (.28)

70. Wu and Palmer say that strep throat and tetanus (.57) are more similar than are bacterial infection and yeast infection (.4)