Measuring Semantic Similarity and Relatedness in the Biomedical Domain : Methods and Applications - presented Feb 21, 2012 as a webinar to the Mayo Clinic BMI group.

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

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.

6. Sentiment Classification

13. General English

15. Unified Medical Language System

20. Least Common Subsumer (LCS)

27. Jiang & Conrath, 1997 (jcn)

28. Lin, 1998 (lin)

31. Assumes all paths have same “weight”

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

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

38. etc...

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

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

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

44. Deeper concepts more specific

45. Paths between deeper concepts travel less semantic distance

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

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

54. Term frequency +Inherited 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)

61. IC (a) + IC (b)

62. Look familiar?

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

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

65. depth(a) + depth (b)

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

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

76. No is-a relations so can't be similarity

80. But, definitions are short, inconsistent

82. Adapted Lesk, Banerjee & Pedersen, 2003

83. Two separate ontologies...

84. Could join them together … ?

85. Each concept has definition

86. Find overlaps in definitions...

92. Alopecia : … a result of cancer treatment

97. Suppose cancer_treatment and chemotherapy don't occur in text with each other. But, suppose that “survival” occurs with each.

98. cancer_treatment and chemotherapy are second order co-occurrences via “survival”

100. Defined concept now represented by an averaged vector of co-occurrences

101. Measure relatedness of concepts via cosine between their respective vectors

106. Depths

107. Information content

108. Definitions

113. Patwardhan et al., 2003 (WordNet)

114. Pedersen and Kolhatkar 2009 (WordNet)

118. http://search.cpan.org/dist/UMLS-SenseRelate/

121. SenseRelate - pairwise

122. SenseRelate – 2 neighbors

125. SenseRelate - WordToSet

127. Jiang-Conrath measure often a high performer for nouns (e.g., Patwardhan et al. 2003)

134. PhD UMTC, 2009

135. Post-doc UMTC, 2009 - 2011

139. Now at Twitter Research

142. Original developer of WordNet::SenseRelate::TargetWord

148. S. Banerjee and T. Pedersen. Extended gloss overlaps as a measure of semantic relatedness. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 805-810, Acapulco, August 2003.

149. J. Caviedes and J. Cimino. Towards the development of a conceptual distance metric for the UMLS. Journal of Biomedical Informatics, 37(2):77-85, April 2004.

150. J. Jiang and D. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings on International Conference on Research in Computational Linguistics, pages 19-33, Taiwan, 1997.

152. M.E. Lesk. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine code from an ice cream cone. In Proceedings of the 5th annual international conference on Systems documentation, pages 24-26. ACM Press, 1986.

153. D. Lin. An information-theoretic definition of similarity. In Proceedings of the International Conference on Machine Learning, Madison, August 1998.

154. B. McInnes, T. Pedersen, Y. Liu, G. Melton and S. Pakhomov. Knowledge-based Method for Determining the Meaning of Ambiguous Biomedical Terms Using Information Content Measures of Similarity. Appears in the Proceedings of the Annual Symposium of the American Medical Informatics Association, pages 895-904, Washington, DC, October 2011.

156. S. Patwardhan, S. Banerjee, and T. Pedersen. Using measures of semantic relatedness for word sense disambiguation. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, pages 241—257, Mexico City, February 2003.

157. S. Patwardhan and T. Pedersen. Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. In Proceedings of the EACL 2006 Workshop on Making Sense of Sense: Bringing Computational Linguistics and Psycholinguistics Together, pages 1-8, Trento, Italy, April 2006.

158. T. Pedersen. Rule-based and lightly supervised methods to predict emotions in suicide notes. Biomedical Informatics Insights, 2012:5 (Suppl. 1):185-193, January 2012.

160. T. Pedersen, S. Pakhomov, S. Patwardhan, and C. Chute. Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics, 40(3) : 288-299, June 2007.

161. R. Rada, H. Mili, E. Bicknell, and M. Blettner. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19(1):17-30, 1989.

163. H. Schütze. Automatic word sense discrimination. Computational Linguistics, 24(1):97-123, 1998.

164. J. Zhong, H. Zhu, J. Li, and Y. Yu. Conceptual graph matching for semantic search. Proceedings of the 10th International Conference on Conceptual Structures, pages 92-106, 2002.

168. However, a path made up of different kinds of relations can lead to big semantic jumps

171. http://search.cpan.org/dist/WebService-UMLSKS-Similarity/