As museums continue to develop more sophisticated techniques for managing and analyzing cultural data, many are beginning to encounter challenges when trying to deal with the nuances of language and automated processing tools. How might user-generated comments be harvested and processed to determine the nature of the comment? Is it possible to use existing collection documentation to derive relations between similar objects? How can we train systems to automatically recognize (disambiguate) different meanings of the same word? Can automated language processing lead to more compelling browsing interfaces for online collections? Luckily, a good deal of expertise and tools exist within the field of computational linguistics that can be applied to these problems to achieve meaningful results. Informed by previous work in computational linguistics and relevant project experience, the authors will address a number of these questions providing insight about how answers to impact museum practice might be found. Authors will share tools and resources that museum software developers can use to prototype and experiment with these techniques - without being experts in language processing themselves. In addition, the authors will describe the work of the T3: Text, Tags, Trust research project and how they have applied these tools to a large shared dataset of object metadata and social tags collected by the Steve.museum project. Specific challenges regarding batch-processing tools and large datasets will be addressed. Best practices and algorithms will be shared for dealing with a number of sticky issues. Directions for future research and promising application areas will be also be discussed. A presentation from Museums and the Web 2011 (MW2011)

1. Your spoken paper cannot be the same as your written paper Read more: Museums and the Web 2011 (MW2011): Presentation Guidelines | conference.archimuse.com

2. Computational Linguistics in Museums: Applications for Cultural Datasets Klavans Judith Susan Robert Chun Stein Guerra Raul

3. ComputationalLinguistics Language - Words, Words, Words Use Meaning Syntax Shape of words Sounds

4. Applications Speech synthesis – 1980’s Talking Machines for the Blind Intelligent search – pre-google Finding names – who, what, where Translation Speech recognition Answering Questions – What is Watson?

5. Domains for Computational Linguistics Healthcare – interpreting patient records Government – helping people find information International Affairs – cross-language translation Law – analyzing Enron scandal email Marketing – Opinions on products Museums – analyzing text and tags associated with objects for better access

6. Computational Linguistics for Metadata Building +

7. Computational Linguistics in Museums: Applications for Cultural Datasets Klavans Judith Susan Robert Chun Stein Guerra Raul

8. InterdisciplinaryResearch Computational Linguistics in Museums

9. Text, Tags, Trust Funded in 2008 by IMLS With the University of Maryland, and collaborative of museum partners Studying the relationships between social tags, scholarly text and resources, and the application of trust networks to improve access to museum collections.

10. MW 2011 Contributions Which Computational Linguistic tools can or should be applied to tags? How do these tools impact tag analysis? What results differ from the initial steve.museum results from Trant 2007? So what – for CL? So what – for Museums?

14. Gallery Label This canvas was the first one Gauguin painted during the two months he spent in Provence.... Gauguin had rebelled against Impressionism's reliance on the visible world, and he altered nature's shapes and colors to suggest his own more subjective reaction to the landscape. While the rural subject and acidic colors show the influence of van Gogh, this image is more indebted to Paul Cézanne. In his careful integration of the haystack and farm buildings, Gauguin has echoed Cézanne's emphasis on geometric form.

15. Tools for Tags Morphological Analysis – Conflate when possible Cats, cat Haystacks, haystack Painting, paint ? What words are verbs, nouns, adjectives? How should multi-word tags be handled?

16. Raw Tags or Tokens

17. Results 25% 93% 68%

18. 1. NN=25205 2. JJ=6319 3. NNS=4041 4. NN_NN=2257 5. JJ_NN=1792 6. VBG=1043 7. VBN=727 8. NP=708 9. OD_NN=454 10. JJ_NNS=413

19. Top 10 POS Patterns: 1. NN=6706 2. NN_NN=1713 3. JJ_NN=1194 4. JJ=921 5. NNS=757 6. JJ_NNS=303 7. NN_NNS=300 8. VBG=238 9. NP=209 10. VBN_NN=202

23. Irecursor to parsing.

26. What About “New England” Idioms / lexicalized phrases are more difficult Heuristic comparison to Wikipedia Titles matched 46% (30% distinct) of multiword tags E.g. “Grapes of Wrath”, “Irish Wolfhound”, “Franco-Prussian War” *Klavans and Golbeck, 2010

27. Wish List - Better ways to tame the proliferation of rich but “noisy” content Clustering over tags for similarity Clustering over tags and terms from text Matching over existing terms to identify meaningful units Apply machine learning techniques to guess meaning Bigrams, Trigram, Thesauri, Corpus Analysis

28. Acknowledgements Steve.museum project members T3 and steve.museum museum partners University of Maryland, T3 group IMA Museum ……and other participants

29. Thank You! Questions?