Presented at the Library Technology Conference 2011 in St. Paul, MN. Program Description: Oral history collections provide a wealth of information, yet current practices in metadata creation and access limit the amount of information within the interview transcripts that can be discovered. This poster describes the Miami University Libraries current project of using Open Source Software in creating enhanced access to our Oral History collection. The Oral History Project at Miami University contains over 100 interviews pertaining to experiences at the University, with transcripts for over half of the interviews. The poster will describe the process of batch processing transcripts using OpenCalais, a web service that automates the creation of metadata for content using natural language processing and machine learning, and displaying both the transcripts and metadata in the content management system Drupal using various modules. We will discuss the results from the comparison of machine generated and human generated metadata in this project and the benefits and concerns surrounding both methods. Future project developments will also be included.

2. Current and former students, faculty, and staff, as well as friends of the University share recollections of their Miami years

4. Uses natural language processing and machine learning to extract categorized metadata (in RDF format) from full text documents

6. Used widely for web sites and blogs

8. Accuracy

9. Completeness

10. Context

11. Consistency

12. Interoperability

15. OC categories provide a less granular browsing structure

16. Terms representing contextual and relational information are lacking in OC results

17. Certain aspects of the OC schema don’t suit the content (many irrelevant categories) and there are numerous gaps when compared to the cataloger created metadata

18. Meaning of many OC categories is ambiguous making index terms difficult to interpret

19. Preservation and genre metadata not captured (since OC only processes text)

20. Subject indexing seems to be a weakness of OC – it only generated a few very broad terms, though it did so with a great deal of accuracy

22. Black Student Action Committee

23. Butler Co. Sheriff

24. Faculty leaving the University

25. Faculty Senate

26. Gentle Revolution

27. Long hair and beards on men

28. Rowan Hall occupation

30. The Organ (false hit)

31. Music

32. Education

33. Jim Zwerg (mis-categorized)

34. Politics

35. Religion

36. Technology (false hit)Even though the following table shows some of the name entries issues from OC, it should be noted that OC on average created more name entries than catalogers.