Fridolin Wild, Gaston Burek, Adriana Berlanga

1. Language Technology Enhanced Learning Fridolin Wild The Open University, UK Gaston Burek University of Tübingen Adriana Berlanga Open University, NL

3. Latent-Semantic Analysis LSA

5. Input (e.g., documents) { M } = Deerwester, Dumais, Furnas, Landauer, and Harshman (1990): Indexing by Latent Semantic Analysis, In: Journal of the American Society for Information Science, 41(6):391-407 Only the red terms appear in more than one document, so strip the rest. term = feature vocabulary = ordered set of features TEXTMATRIX

6. Singular Value Decomposition =

7. Truncated SVD … we will get a different matrix (different values, but still of the same format as M). latent-semantic space

8. Reconstructed, Reduced Matrix m4: Graph minors : A survey

9. Similarity in a Latent-Semantic Space (Landauer, 2007) Query Target 1 Target 2 Angle 2 Angle 1 Y dimension X dimension

11. (Landauer, 2007)

12. Configurations 4 x 12 x 7 x 2 x 3 = 2016 Combinations

14. The Statistical Language and Environment R R

20. A Simple Evaluation of Students Writings Feedback

21. Evaluating Student Writings External Validation? Compare to Human Judgements! (Landauer, 2007)

25. Concept-Focused Evaluation (using http://eczemablog.blogspot.com/feeds/posts/default?alt=rss)

26. Visualising Lexical Semantics Topic Proxy

28. Classical Landauer Example tl = landauerSpace$tk %*% diag(landauerSpace$sk) dl = landauerSpace$dk %*% diag(landauerSpace$sk) dtl = rbind(tl,dl) s = cosine(t(dtl)) s[which(s<0.8)] = 0 plot( network(s), displaylabels=T, vertex.col = c(rep(2,12), rep(3,9)) )

29. Divisive Clustering (Diana)

32. Permutating Permutation

37. Challenging Questions Discussion

39. Questions? #eof.