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December 7 th , 2010, Shanghai, China Latent Semantics and  Social Interaction Fridolin Wild KMi, The Open University
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Information what is  Meaning could be the  quality of a certain signal. Meaning could be a  logical abstractor = a release mechanism. (96dpi) meaning
meaning  is social ,[object Object],Network effects make a network of shared understandings more valuable with growing size:  allowing e.g. ‘distributed cognition’. ,[object Object]
 
Concepts & Competence things we can (not) construct from language ,[object Object],[object Object],[object Object],[object Object],I have been convincingly Sapir-Whorfed by this book.
A “Semantic Community” LSA SNA Associative  Closeness Concept  (disambiguated term) Person Social relation
LSA
Latent Semantic Analysis ,[object Object],[object Object],“ 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
latent-semantic space Singular values (factors, dims, …) Term Loadings Document Loadings
(Landauer, 2007)
Associative Closeness You need factor stability? > Project using fold-ins! Term 1 Document 1 Document 2 Angle 2 Angle 1 Y dimension X dimension
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
Reconstructed, Reduced Matrix m4:  Graph   minors : A  survey
doc2doc - similarities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
(S)NA
Social Network Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Constructing  a network  from raw data forum postings incidence matrix IM adjacency matrix AM IM x  IM T
Visualization:  Sociogramme
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)
Example: Joint virtual  meeting attendance (Flashmeeting co-attendance in  the Prolearn Network of Excellence)
Example: Subscription structures  in a blogging network  (2 nd  trial of the iCamp project)
MIA
Meaningful Interaction Analysis (MIA) ,[object Object],[object Object],[object Object]
The mathemagics behind Meaningful Interaction Analysis
Network Analysis
MIA of the classic Landauer
 
 
 
Applications
Capturing traces in text: medical student case report
Internal latent-semantic graph structure (MIA output)
 
Evaluating Chats with PolyCAFe
 
Conclusion
Conclusion ,[object Object],[object Object],[object Object],[object Object]
#eof.
Contextualised Doc & Term Vectors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Speed: use existing space and fold in e.g. author vectors latent-semantic space
Influencing  Parameters  (LSA) Pearson(eu, österreich) Pearson(jahr, wien)

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Meaningful Interaction Analysis