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Latent Semantics & Social Interaction



Guest lecture given at the Goethe University in Frankfurt.

Guest lecture given at the Goethe University in Frankfurt.



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    Latent Semantics & Social Interaction Latent Semantics & Social Interaction Presentation Transcript

    • November 18th, 2010, Frankfurt, Germany Latent Semantics and Social Interaction Fridolin Wild KMi, The Open University
    • Outline
      • Context & Framing Theories
      • Latent Semantic Analysis (LSA)
      • (Social) Network Analysis (S/NA)
      • Meaningful Interaction Analysis (MIA)
      • Outlook: Analysing Practices
      • Context & Theories
    • 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
        • To develop a shared understanding is a natural and necessary process, because language underspecifies meaning: future understanding builds on it
      Network effects make a network of shared understandings more valuable with growing size: allowing e.g. ‘distributed cognition’.
        • At same time: linguistic relativity (Sapir-Whorf hypothesis): our own language culture restricts our thinking
    • 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.
    • A “Semantic Community” LSA SNA Associative Closeness Concept (disambiguated term) Person Social relation
    • LSA
    • 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
    • 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
        • Unreduced = pure vector space model
        • - Based on M = TSD’
        • - Pearson Correlation over document vectors
        • reduced
        • - based on M 2 = TS 2 D’
        • - Pearson Correlation over document vectors
    • (S)NA
    • 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)
    • 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)
      • 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
    • The mathemagics behind Meaningful Interaction Analysis
    • Contextualised Doc & Term Vectors
      • T k = left-hand sided matrix = ‚term loadings‘ on the singular value
      • D k = right-hand sided matrix = ‚document loadings‘ on the singular value
      • Multiply them into same space
        • V T = T k S k
        • V D = D k T S k
      • Cosine Closeness Matrix over ... = adjacency matrix = a graph
      • More: e.g. add author vectors V A through cluster centroids or vector addition of their publication vectors
      Speed: use existing space and fold in e.g. author vectors latent-semantic space
    • Network Analysis
    • MIA of the classic Landauer
    • Influencing Parameters (LSA) Pearson(eu, österreich) Pearson(jahr, wien)
    • Capturing traces in text: medical student case report
    • Internal latent-semantic graph structure (MIA output)
    • Outlook: Practices?
    • Mash-Up Personal Learning Environment
    • Conclusion
    • 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)
    • #eof.