November 18th, 2010, Frankfurt, Germany

Latent Semantics and Social Interaction

Fridolin WildKMi, The Open University

<2>

OutlineContext & Framing Theories

Latent Semantic Analysis (LSA)

(Social) Network Analysis (S/NA)

Meaningful Interaction Analysis (MIA)

Outlook: Analysing Practices

<3>

Context & Theories

<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>

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>

<7>

Concepts & Competencethings 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>

A “Semantic Community”

Associative Closeness

Concept (disambiguated term)

Person

Social relation

LSA SNA

<9>

LSA

<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>

latent-semantic space

Singular values (factors, dims, …)

TermLoadings

Document Loadings

<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>

Associative Closeness

m

ii

m

ii

m

iii

ba

ba

1

2

1

2

1cos

Term 1

Document 1

Document 2Angle 2

Angle 1

Y d

ime

ns

ion

X dimension

You need factor stability?

> Project using fold-ins!

<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>

Reconstructed, Reduced Matrix

m4: Graph minors: A survey

<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>

(S)NA

<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>

Constructing a network

from raw data

forum postings

incidence matrix IM

adjacency matrix AMIM x IMT

<20>

Visualization: Sociogramme

<21>

Measuring Techniques (Sample)

Degree Centralitynumber of (in/out) connections to others

Closenesshow close to all others

Betweennesshow often intermediary

Componentse.g. kmeans cluster (k=3)

<22>

Example: Joint virtual meeting attendance(Flashmeeting co-attendance in the Prolearn Network of Excellence)

<23>

Example: Subscription structures in a blogging network (2nd trial of the iCamp project)

<24>

MIA

<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 onlyor in spaces with additional and different (!) relations

<26>

The mathemagics behindMeaningful Interaction Analysis

<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 = DkT

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>

Network Analysis

<29>

MIA of the classic Landauer

<30>

Influencing Parameters

(LSA)

Pearson(eu, österreich)

Pearson(jahr, wien)

<31>

<32>

<33>

<34>

Capturing traces in text: medical student case report

<35>

Internal latent-semantic graph structure (MIA output)

<36>

<37>

Outlook: Practices?

<38>

<39>

Mash-Up Personal Learning

Environment

<40>

<41>

<42>

Conclusion

<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>

#eof.