Vivien Petras UC Berkeley School of Information

1. Translating Dialects in Search: Mapping between Specialized Languages of Discourse and Documentary Languages Vivien Petras UC Berkeley School of Information

2. Overcoming the Language Problem in Search How can someone searching for violins be made aware that there are also fiddles (and vice versa)?

3. Outline • The Language Problem in Information Retrieval • Dialects & Contexts • The Search Term Recommender • 4 Research Questions • Exploratory Web Interface

4. IR = Language Mapping Exercise Concept Space Concept Space Match! Text Question Search Statement Document Author Searcher • Mapping between searcher and IR system • Mapping between author and IR system • Mapping between search statement and document

5. IR = Language Mapping Exercise Concept Space Match! Question Search Statement Document Searcher Information Retrieval • A search statement needs to describe the: • searcher’s question (information need) • documents that are relevant to a searcher’s question

6. The Language Problem In Semiotics:  Unlimited semiosis In Information Science: Inter-indexer inconsistency

7. Dialects and Contexts How to alleviate language ambiguity for search term selection?

8. Dialects and Contexts How to alleviate language ambiguity for search term selection? Wittgenstein Philosophy of language: Language is disambiguated within contexts and specialized dialects.

9. Dialects and Contexts How to alleviate language ambiguity for search term selection? Wittgenstein Philosophy of language: Language is disambiguated within contexts and specialized dialects. Support search term selection:

10. Dialects and Contexts How to alleviate language ambiguity for search term selection? Wittgenstein Philosophy of language: Language is disambiguated within contexts and specialized dialects. Support search term selection: • Within the dialect of a specialized community

11. Dialects and Contexts How to alleviate language ambiguity for search term selection? Wittgenstein Philosophy of language: Language is disambiguated within contexts and specialized dialects. Support search term selection: • Within the dialect of a specialized community • In context

12. Dialects and Contexts How to alleviate language ambiguity for search term selection? Wittgenstein Philosophy of language: Language is disambiguated within contexts and specialized dialects. Support search term selection: • Within the dialect of a specialized community • In context • Using the language of documents (for term matching)

13. Search Term Recommender Did you mean… Specialty Specialty Search Statement Specialty Specialty Specialty Specialty Term Specialty Term Specialty Term Specialty Term Specialty Specialty Information Collection

14. Search Term Recommender

15. The Search Term Recommender: Applications • Term selection support (query expansion & reformulation) • Automatic classification • Terminology mapping

16. The Search Term Recommender - Questions • How can specialties & specialty dialects be identified in an information collection?

17. The Search Term Recommender - Questions • How can specialties & specialty dialects be identified in an information collection? • Do specialty dialects really differ?

18. The Search Term Recommender - Questions • How can specialties & specialty dialects be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects?

19. The Search Term Recommender - Questions • How can specialties & specialty dialects be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects? • How specific should specialties be?

20. The Search Term Recommender - Questions • How can specialties & specialty dialects be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects? • How specific should specialties be? • Tested on 2 bibliographic databases: • Inspec • Medline (Ohsumed collection)

21. Inspec • Physics, Electrical and Electronic Engineering, Computers and Control • Document: author, title, source, publication year, abstract, Inspec thesaurus descriptors, Inspec classification codes • Test collection:

22. Medline Ohsumed Collection • Biomedicine and Health • Document: author, title, source, publication year, publication type, abstract, Mesh Headings • Test collection:

23. The Search Term Recommender System - Questions • How can specialties be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects? • How specific should specialties be? • Tested on 2 bibliographic collections: • Inspec • Medline (Ohsumed collection)

24. Determine specialty documents in the collection: • Domain terminology

25. Determine specialty documents in the collection: • Domain terminology • Publication source

26. Determine specialty documents in the collection: • Domain terminology • Publication source • Bibliometric analysis

27. Determine specialty documents in the collection: • Domain terminology • Publication source • Bibliometric analysis • Social network analysis

28. Determine specialty documents in the collection: • Domain terminology • Publication source • Bibliometric analysis • Social network analysis • Subject-specific classification

29. Identification of Specialties in an Information Collection Inspec test collection • by top-level categories in the Inspec classification • 3 specialties: Physics, Electrical & Electronic Engineering, Computers & Control

30. Identification of Specialties in an Information Collection Inspec test collection • by top-level categories in the Inspec classification • 3 specialties: Physics, Electrical & Electronic Engineering, Computers & Control Ohsumed test collection • by journals grouped by subject • 33 specialties

31. The Search Term Recommender System - Questions • How can specialties be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects? • How specific should specialties be? • Tested on 2 bibliographic collections: • Inspec • Medline (Ohsumed collection)

32. Differences in Language • Differences in specialty dialects (specialty term overlap)

33. Differences in Language • Differences in specialty dialects (specialty term overlap) • Differences in documentary languages (subject metadata term overlap)

34. Differences in Language • Differences in specialty dialects (specialty term overlap) • Differences in documentary languages (subject metadata term overlap) • Differences in search term recommender suggestions (term suggestion overlap)

35. Inspec Dialects (specialty term overlap) terms analyzed: 60,601 Subject metadata term overlap: 87% Suggested term overlap: 30%

36. Ohsumed Dialects (Specialty term overlap) terms analyzed: 11,663 Subject metadata term overlap: 32% Suggested term overlap: 30%

37. The Search Term Recommender System - Questions • How can specialties be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects? • How specific should specialties be? • Tested on 2 bibliographic collections: • Inspec • Medline (Ohsumed collection)

38. Automatic classification • Suggest subject metadata for documents • Comparison: specialty vs. general term suggestions

39. Automatic Classification Title: “A search for clusters of protostars in Orion cloud cores”

40. Automatic Classification Title: “A search for clusters of protostars in Orion cloud cores” Evaluation

41. Performance of the STR: Inspec First 3 suggested: Recall: 13.6% Precision: 11.2% Test Documents: 42,735 Specialties: 3

42. Performance of the STR: Ohsumed First 3 suggested: Recall: 26% Precision: 25.6% Test Documents: 18,733 Specialties: 33

43. The Search Term Recommender System - Questions • How can specialties be identified in an information collection? • Do specialty dialects really differ? • Is performance improved when focusing on specialty dialects? • How specific should specialties be? • Tested on 2 bibliographic collections: • Inspec • Medline (Ohsumed collection)

44. Specificity of Specialties • Language differences • Representative sample of specialty language for training

45. Specificity of Specialties - Inspec Identifying subspecialties by classification hierarchy • e.g. Computers & Control -- Computer Hardware -- Circuits & Devices

46. Specificity of Specialties - Inspec Identifying subspecialties by classification hierarchy • e.g. Computers & Control -- Computer Hardware -- Circuits & Devices Test documents: 2425 Specialties: 3

47. Specificity of Specialties - Ohsumed Identifying subspecialties by journal within subject • e.g. Orthopedics -- Clinical Orthopaedics & Related Research journal

48. Specificity of Specialties - Ohsumed Identifying subspecialties by journal within subject • e.g. Orthopedics -- Clinical Orthopaedics & Related Research journal Test documents: 745 Specialties: 3

49. Exploratory Web Interfaces Inspec http://metadata.sims.berkeley.edu/str/inspec/inspec.html Ohsumed http://metadata.sims.berkeley.edu/str/ohsumed/ohsumed.html

50. Summary • How can specialties be identified in an information collection? • Inspec: subject-specific classification • Ohsumed: journal specialty area