Post on 24-May-2015
description
Subjects on Objects in Contexts:
Using GICA method to quantify epistemological subjectivity
Timo Honkela1, Juha Raitio1, Krista Lagus1,Ilari T. Nieminen1, Nina Honkela2, Mika Pantzar3
1Aalto University(former Helsinki University of Technology)
Department of Information and Computer Science(former Neural Networks Research Center, Adaptive Informatics Research Center)
2University of Helsinki
3National Consumer Research Center
Finland
Subjects on Objects in Contexts:
Using GICA method to quantify epistemological subjectivity
Timo Honkela Juha Raitio Krista Lagus
Ilari T. Nieminen Nina Honkela Mika Pantzar
Traditional representation of meaning:Generalized (non-contextual, non-subjective)
http://pages.cpsc.ucalgary.ca/~gaines/reports/KBS/VLL/
Gaines: “Designing Visual Languages for Description Logics”
Meaning is contextual
red winered skinred shirt
Gärdenfors: Conceptual Spaces
Hardin Color for Philosophers
Meaning is contextual
SNOW -WHITE?
WHITE
Meaning is contextual
● “Small”, “big”● “White house”● “Get”● “Every” - “Every Swede is tall/blond”● etc. etc.
Learning meaning from context
Honkela, Pulkki & Kohonen 1995
● Self-Organizing Semantic Maps(Ritter & Kohonen 1989)
● Latent Semantic Analysis● Latent Dirichlet Allocation● WordICA● etc. etc.
Learning meaning from context:Maps of words in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995
Meaning is subjective
Meaning is subjective
● Good● Fair● Useful● Scientific● Democratic● Sustainable● etc.
A proper theory ofmeaning has to takethis into account.
(opposite to the view given by V. Cherkassky about an hour ago)
Modeling variation ofmeaning in a community of agents
(Lindh-Knuutila, Lagus & Honkela, SAB'06)Related to e.g. Steels and Vogt on language games
Honkela: ICANN 1993
Steels, Kaplan, Vogt, et al.:Language games
Intersubjective Concept Spaces
Receiver(agent 2)
signald
(shared)context
Sender(agent 1)
observations
conceptspace
C2
symbolspace
S2
conceptspace
C1
symbolspace
S1
(Honkela, Könönen, Lindh-Knuutila & Paukkeri 2008)
Intersubjective Concept Spaces
λ : Ci × Cj → R, i ≠ jA distance between two points in the concept spaces of different agents
S: symbol space,The vocabulary of anagent that consists of discrete symbols
: sξ i S∈ i → CAn individual mapping function from symbols to concepts
φi: Si D→An individual mapping from agent i's vocabulary to the signal space D andan inverse mappingφ1
i from the signal space to the symbol space
Ci: Ndimensional metric concept space
Observing f1 and after symbol selection process, agent 1 communicates a symbol s*to agent 2 as signal d. When agent 2 observes d, it maps it to some s2 S∈ 2 by using the function φ 1
1. Then it maps the symbol to some point in its concept space by using ξ2. If this point is close to its observation f2 in the sense of λ, the communication process has succeeded.
(Honkela, Könönen, Lindh-Knuutila & Paukkeri 2008)
Gary B. Fogel11th of June, 2012
WCCI 2012
GICA:Grounded
Intersubjective Concept Analysis
Description of the method
Subjectifying: adding subjective views into object-context matrices
Outcome: Subject-Object-Context (SOC) Tensors
More on subjectification
● A central question in GICA is how to obtain the data on subjectivity for expanding an object-context matrix into the tensor that accounts additionally for subjectivity.
● The basic idea is that for each element in the object-context matrix one needs several subjective evaluations.
● Specifically, the GICA data collection measures for each subject s
i the relevance x
ijk of
an object oj in a context c
k
Potential sources for subjectification
● Conceptual surveys: ● individual assessment of contextual
appropriateness
● Text mining:● statistics of word/phrase-context patterns
● Empirical psychology:● reaction times, etc.
● Brain research
Flattening: unfolding 3-way tensorfor traditional 2-way analysis
GICA:Grounded
Intersubjective Concept Analysis
Examples of use
Case 1: Wellbeing concepts
● A conceptual survey was conducted among the participants of the EIT ICT Labs activity “Wellbeing Innovation Camp” that took place between 26th and 29th of October 2010 in Vierumäki, Finland.
● The participants were asked to fill in a data matrix that consisted of the objects as rows and the contexts as columns.
● Each individual’s task was to determine how strongly an object is associated with a context, using Likert scale from 1 to 5
OBJECTS:
Relaxation
Happiness
Fitness
Wellbeing
CONTEXTS:
SUBJECTS: Event participants
Data collection
MDS: Objects x Subjects
Fitness
NeRV: Objects x Subjects
Fitness
J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.
NeRV:
SOM: Objects x Subjects
SOM: Distribution of contexts
SOM: Contexts
Case 2: State of the Union Addresses
● In this case, text mining is used for populating the Subject-Object-Context tensor
● This took place by calculating the frequencies on how often a subject uses an object word in the context of a context word● Context window of 30 words
SOM: Subjects (presidents)
SOM: Objects x Subjects
Analysis of the word 'health'
Related research andfuture plans
Our related research on subjectivity:User-specific difficulty assessment
Paukkeri, Ollikainen & Honkela, InformationProcessing & Management,2012
Interoperability
● Current situation:
Formalization and harmonization of knowledge representations (e.g. using XML)
● Future possibility:
Meaning negotiation between systemsbased on SOC tensors and furtherdevelopments
Context data is important!
Enhanced communication, democratic and participatory processes
Collaboration opportunities
● Theoretical work● Interdisciplinary: brain research, psychology,
sociology, organization research, etc.● Methodological
– Formulation in different theoretical frameworks– Analogical development with crisp>fuzzy:
“objective”>subjective
● Experimental● Case studies
● Research visits, tutorials, workshops● GICA workshop/summer school in 2013 in Finland