fractal composition of meaning: toward a collage theorem for language simon d. levy department of...
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Fractal Composition of Meaning:Fractal Composition of Meaning:Toward a Collage Theorem for Toward a Collage Theorem for LanguageLanguage
Simon D. Levy Simon D. Levy
Department of Computer ScienceDepartment of Computer Science
Washington and Lee UniversityWashington and Lee University
Lexington, VA 24450Lexington, VA 24450
http://www.cs.wlu.edu/~levyhttp://www.cs.wlu.edu/~levy
Part I: Self-SimilarityPart I: Self-Similarity
... But I know, too,
That
the blackbird is involved
in
what what
... But I know, too,
That the blackbird is involved
In what
– Wallace Stevens
I know.
Part II: A Little MathPart II: A Little Math
Iterated Function SystemsIterated Function Systems
• IFS: Another way to make a fractal
• Start with an arbitrary initial image
• Apply a set of contractive affine transforms
• Repeat until image no longer changes
• E.g., Sierpinski Triangle...
A02
T i ,i 1,2,. .. ,n
Sierpinski TriangleSierpinski Triangle
T 1xy
.5 00 .5
xy
00
T 2xy
.5 00 .5
xy
0.5
T 3xy
.5 00 .5
xy
.5
.5
I.e., three half-size copies, one in each of three corners of [0,1] 2
Sierpinski Triangle: 0 IterationsSierpinski Triangle: 0 Iterations
Sierpinski Triangle: 1 IterationSierpinski Triangle: 1 Iteration
Sierpinski Triangle: 2 IterationsSierpinski Triangle: 2 Iterations
Sierpinski Triangle: 3 IterationsSierpinski Triangle: 3 Iterations
Sierpinski Triangle: 4 IterationsSierpinski Triangle: 4 Iterations
Sierpinski Triangle: 5 IterationsSierpinski Triangle: 5 Iterations
Sierpinski Triangle: 6 IterationsSierpinski Triangle: 6 Iterations
Sierpinski Triangle: 7 IterationsSierpinski Triangle: 7 Iterations
Sierpinski Triangle: New Initial Sierpinski Triangle: New Initial ImageImage
Sierpinski TriangleSierpinski Triangle
Sierpinski TriangleSierpinski Triangle
Sierpinski TriangleSierpinski Triangle
Sierpinski TriangleSierpinski Triangle
Sierpinski TriangleSierpinski Triangle
Sierpinski TriangleSierpinski Triangle
Sierpinski TriangleSierpinski Triangle
IFS Fractals in NatureIFS Fractals in Nature
Fractal Image CompressionFractal Image Compression
• Doesn't matter what image we start with
• All information needed to represent final “target”
image is contained in transforms
• Instead of storing millions of pixels, determine
transforms for target image, and store them
• How to determine transforms?
T i
A0
The Collage TheoremThe Collage Theorem
• Let
• is the attractor or “fixed point” of
• Collage Theorem (Barnsley 1988): Given
arbitrary target image , transforms encoding
are s.t.
• Use various practical methods to find
f A0iT i A0
limn
f n A0 f
A A
T i f AiT i A A
T i
Practical Fractal Image Practical Fractal Image CompressionCompression
• Most real-world images are only partially self-similar
• Arbitrary images can be partitioned into “tiles”,
each associated with a transform.
• Compression algorithm computes and stores
locations and transforms of tiles
Practical Fractal Image Practical Fractal Image CompressionCompression
Practical Fractal Image Practical Fractal Image CompressionCompression
Part III: UnificationPart III: Unification
The “Two Cultures”The “Two Cultures”
Discrete Symbols
Semantic Relations
Grammar Rules
Graph StructuresContinuous
Vectors
Metric Spaces
Continuous TransformsImages
Linguistics AI
Logic
Dynamical Systems
ChaosElectrical Engineering
Meanings as VectorsMeanings as Vectors“You shall know a word by the company it keeps”
– J. R. Firth
• Vector representation of a word encodes co-occurrence with other words
• Latent Semantic Analysis (Indexing) –
Singular Value Decomposition of co-
occurrence matrix on text; 300-dimensional
vectors [Landauer, Dumais, Kintsch]
Meanings as VectorsMeanings as Vectors
• Self-Organizing Maps – Collapse high-dimensional descriptions (binary features or real-val vectors) into 2-D [Kohonen]
• Simple Recurrent Networks – Hidden-
variable temporal model predicting next word
based on current; 150-D vectors [Elman]
Meanings as VectorsMeanings as Vectors
Fred says the woman arrived.The woman says fred arrived.Fred loves the woman.The woman loves fred.The woman arrived.Fred arrived.
Composing Meaningful Vectors Composing Meaningful Vectors
the woman.
Composing Meaningful Vectors Composing Meaningful Vectors
loves the woman.
Composing Meaningful Vectors Composing Meaningful Vectors
Fred loves the woman.
Part IV: ConclusionsPart IV: Conclusions
Advantages of Vector RepresentationsAdvantages of Vector Representations
• Meaning as a gradient phenomenon (semantic spaces = vector spaces)
• Can represent all transforms with a single
hidden-variable non-linear equation (“grammar”)
• Gradient-descent methods as learning model
• Principled, biologically plausible alternative to
“Words and Rules” approach [Chomsky, Pinker,
Fodor]
A Collage Theorem for LanguageA Collage Theorem for Language
A Collage Conjecture for LanguageA Collage Conjecture for Language
A Collage Conjecture for LanguageA Collage Conjecture for Language
A Collage Hypothesis for LanguageA Collage Hypothesis for Language
A Collage Hypothesis for LanguageA Collage Hypothesis for Language
A Collage S.W.A.G. for LanguageA Collage S.W.A.G. for Language
• Words/meanings are co-occurrence vectors.
• Compositions of meanings are transients to
words.
• “Correct” set of transients is one for which
word vectors form a subset of the attractor.