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Latent Semantics & Social Interaction

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Guest lecture given at the Goethe University in Frankfurt.

Guest lecture given at the Goethe University in Frankfurt.

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  • 1. November 18th, 2010, Frankfurt, Germany Latent Semantics and Social Interaction Fridolin Wild KMi, The Open University
  • 2. <2> Outline Context & Framing Theories Latent Semantic Analysis (LSA) (Social) Network Analysis (S/NA) Meaningful Interaction Analysis (MIA) Outlook: Analysing Practices
  • 3. <3> Context & Theories
  • 4. <4> what is Information Meaning could be the quality of a certain signal. Meaning could be a logical abstractor = a release mechanism. (96dpi) meaning
  • 5. <5> meaning is social Network effects make a network of shared understandings more valuable with growing size: allowing e.g. ‘distributed cognition’. To develop a shared understanding is a natural and necessary process, because language underspecifies meaning: future understanding builds on it At same time: linguistic relativity (Sapir-Whorf hypothesis): our own language culture restricts our thinking
  • 6. <6>
  • 7. <7> Concepts & Competence things we can (not) construct from language  Tying shoelaces  Douglas Adams’ ‘meaning of liff’:  Epping: The futile movements of forefingers and eyebrows used when failing to attract the attention of waiters and barmen.  Shoeburyness: The vague uncomfortable feeling you get when sitting on a seat which is still warm from somebody else's bottom I have been convincingly Sapir-Whorfed by this book.
  • 8. <8> A “Semantic Community” Associative Closeness Concept (disambiguated term) Person Social relation LSA SNA
  • 9. <9> LSA
  • 10. <10> Latent Semantic Analysis Two-Mode factor analysis of the co- occurrences in the terminology Results in a latent- semantic vector space “Humans learn word meanings and how to combine them into passage meaning through experience with ~paragraph unitized verbal environments.” “They don’t remember all the separate words of a passage; they remember its overall gist or meaning.” “LSA learns by ‘reading’ ~paragraph unitized texts that represent the environment.” “It doesn’t remember all the separate words of a text it; it remembers its overall gist or meaning.” -- Landauer, 2007
  • 11. <11> latent-semantic space Singular values (factors, dims, …) Term Loadings Document Loadings
  • 12. <12> The meaning of "life" = 0.0465 -0.0453 -0.0275 -0.0428 0.0166 -0.0142 -0.0094 0.0685 0.0297 -0.0377 -0.0166 -0.0165 0.0270 -0.0171 0.0017 0.0135 -0.0372 -0.0045 -0.0205 -0.0016 0.0215 0.0067 -0.0302 -0.0214 -0.0200 0.0462 -0.0371 0.0055 -0.0257 -0.0177 -0.0249 0.0292 0.0069 0.0098 0.0038 -0.0041 -0.0030 0.0021 -0.0114 0.0092 -0.0454 0.0151 0.0091 0.0021 -0.0079 -0.0283 -0.0116 0.0121 0.0077 0.0161 0.0401 -0.0015 -0.0268 0.0099 -0.0111 0.0101 -0.0106 -0.0105 0.0222 0.0106 0.0313 -0.0091 -0.0411 -0.0511 -0.0351 0.0072 0.0064 -0.0025 0.0392 0.0373 0.0107 -0.0063 -0.0006 -0.0033 -0.0403 0.0481 0.0082 -0.0587 -0.0154 -0.0342 -0.0057 -0.0141 0.0340 -0.0208 -0.0060 0.0165 -0.0139 0.0060 0.0249 -0.0515 0.0083 -0.0303 -0.0070 -0.0033 0.0408 0.0271 -0.0629 0.0202 0.0101 0.0080 0.0136 -0.0122 0.0107 -0.0130 -0.0035 -0.0103 -0.0357 0.0407 -0.0165 -0.0181 0.0369 -0.0295 -0.0262 0.0363 0.0309 0.0180 -0.0058 -0.0243 0.0038 -0.0480 0.0008 -0.0064 0.0152 0.0470 0.0071 0.0183 0.0106 0.0377 -0.0445 0.0206 -0.0084 -0.0457 -0.0190 0.0002 0.0283 0.0423 -0.0758 0.0005 0.0335 -0.0693 -0.0506 -0.0025 -0.1002 -0.0178 -0.0638 0.0513 -0.0599 -0.0456 -0.0183 0.0230 -0.0426 -0.0534 -0.0177 0.0383 0.0095 0.0117 0.0472 0.0319 -0.0047 0.0534 -0.0252 0.0266 -0.0210 -0.0627 0.0424 -0.0412 0.0133 -0.0221 0.0593 0.0506 0.0042 -0.0171 -0.0033 -0.0222 -0.0409 -0.0007 0.0265 -0.0260 -0.0052 0.0388 0.0393 0.0393 0.0652 0.0379 0.0463 0.0357 0.0462 0.0747 0.0244 0.0598 -0.0563 0.1011 0.0491 0.0174 -0.0123 0.0352 -0.0368 -0.0268 -0.0361 -0.0607 -0.0461 0.0437 -0.0087 -0.0109 0.0481 -0.0326 -0.0642 0.0367 0.0116 0.0048 -0.0515 -0.0487 -0.0300 0.0515 -0.0312 -0.0429 -0.0582 0.0730 -0.0063 -0.0479 0.0230 -0.0325 0.0240 -0.0086 -0.0401 0.0747 -0.0649 -0.0658 -0.0283 -0.0184 -0.0297 -0.0122 -0.0883 -0.0138 -0.0072 -0.0250 -0.1139 -0.0172 0.0507 0.0252 0.0307 -0.0821 0.0328 0.0584 -0.0216 0.0117 0.0801 0.0186 0.0088 0.0224 -0.0079 0.0462 -0.0273 -0.0792 0.0127 -0.0568 0.0105 -0.0167 0.0923 -0.0843 0.0836 0.0291 -0.0201 0.0807 0.0670 0.0592 0.0312 -0.0272 -0.0207 0.0028 -0.0092 0.0385 0.0194 -0.0451 0.0002 -0.0041 0.0203 0.0313 -0.0093 -0.0444 0.0142 -0.0458 0.0223 -0.0688 -0.0334 -0.0361 -0.0636 0.0217 -0.0153 -0.0458 -0.0322 -0.0615 -0.0206 0.0146 -0.0002 0.0148 -0.0223 0.0471 -0.0015 0.0135 (Landauer, 2007)
  • 13. <13> Associative Closeness ∑∑ ∑ == = =Θ m i i m i i m i ii ba ba 1 2 1 2 1 cos Term 1 Document 1 Document 2 Angle 2 Angle 1 Ydimension X dimension You need factor stability? > Project using fold-ins!
  • 14. <14> Example: Classic Landauer { 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
  • 15. <15> Reconstructed, Reduced Matrix m4: Graph minors: A survey
  • 16. <16> doc2doc - similarities Unreduced = pure vector space model - Based on M = TSD’ - Pearson Correlation over document vectors reduced - based on M2 = TS2D’ - Pearson Correlation over document vectors
  • 17. <17> (S)NA
  • 18. <18> Social Network Analysis  Existing for a long time (term coined 1954)  Basic idea:  Actors and Relationships between them (e.g. Interactions)  Actors can be people (groups, media, tags, …)  Actors and Ties form a Graph (edges and nodes)  Within that graph, certain structures can be investigated • Betweenness, Degree of Centrality, Density, Cohesion • Structural Patterns can be identified (e.g. the Troll)
  • 19. <19> Constructing a network from raw data forum postings incidence matrix IM adjacency matrix AM IM x IMT
  • 20. <20> Visualization: Sociogramme
  • 21. <21> Measuring Techniques (Sample) Degree Centrality number of (in/out) connections to others Closeness how close to all others Betweenness how often intermediary Components e.g. kmeans cluster (k=3)
  • 22. <22> Example: Joint virtual meeting attendance (Flashmeeting co-attendance in the Prolearn Network of Excellence)
  • 23. <23> Example: Subscription structures in a blogging network (2nd trial of the iCamp project)
  • 24. <24> MIA
  • 25. <25> Meaningful Interaction Analysis (MIA)  Combines latent semantics with the means of network analysis  Allows for investigating associative closeness structures at the same time as social relations  In latent-semantic spaces only or in spaces with additional and different (!) relations
  • 26. <26> The mathemagics behind Meaningful Interaction Analysis
  • 27. <27> Contextualised Doc & Term Vectors  Tk = left-hand sided matrix = ‚term loadings‘ on the singular value  Dk = right-hand sided matrix = ‚document loadings‘ on the singular value  Multiply them into same space  VT = Tk Sk  VD = Dk T Sk  Cosine Closeness Matrix over ... = adjacency matrix = a graph  More: e.g. add author vectors VA through cluster centroids or vector addition of their publication vectors latent-semantic space DT VV ∪ ADT VVV ∪∪ Speed: use existing space and fold in e.g. author vectors
  • 28. <28> Network Analysis
  • 29. <29> MIA of the classic Landauer
  • 30. <30> Influencing Parameters (LSA) Pearson(eu, österreich) Pearson(jahr, wien)
  • 31. <31>
  • 32. <32>
  • 33. <33>
  • 34. <34> Capturing traces in text: medical student case report
  • 35. <35> Internal latent-semantic graph structure (MIA output)
  • 36. <36>
  • 37. <37> Outlook: Practices?
  • 38. <38>
  • 39. <39> Mash-Up Personal Learning Environment
  • 40. <40>
  • 41. <41>
  • 42. <42> Conclusion
  • 43. <43> Conclusion  Both LSA and SNA alone are not sufficient for a modern representation theory  MIA provides one possible bridge between them  It is a powerful technique  And it is simple to use (in R)
  • 44. <44> #eof.

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