CASE STUDY II: Modeling the Mental Lexicon

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CASE STUDY II Modeling the Mental Lexicon
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Metal Lexicon (ML) Basics

• It refers to the repository of the word forms
  that resides in the human brain
• Two Questions
• How words are stored in the long term memory,
  i.e., the organization of the ML.
• How are words retrieved from the ML (lexical
  access)
• The above questions are highly inter-related
  to predict the organization one can investigate
  how words are retrieved and vice versa.

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Different Possible Ways of Organization

• Un-organized (a bag full of words) or,
• Organized
• By sound (phonological similarity)
• E.g., start the same banana, bear, bean
• End the same look, took, book
• Number of phonological segments they share
• By Meaning (semantic similarity)
• Banana, apple, pear, orange
• By age at which the word is acquired
• By frequency of usage
• By POS
• Orthographically

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The Hierarchical Model of ML

• Proposed by Collins and Quillian in 1969
• Concepts are organized in a hierarchy
• Taxonomic and attributive relations are
  represented
• Cognitive Economy Put the attributes at the
  highest of all appropriate levels e.g.,
  reproduces applies to the whole animal kingdom

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Hierarchical Model

• According to the principle of cognitive economy
• Animals eat lt mammals eat lt humans eat
• However, shark is a fish salmon is a fish
• What do lt and mean?
• lt Less time to judge
• Equal time to judge

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Spreading Activation Model of ML

• Not a hierarchical structure but a web of
  inter-connected nodes (first proposed by Collins
  and Loftus in 1975)
• Distance between nodes is determined by the
  structural characteristics of the word-forms (by
  sound, by meaning, by age, by )
• Combining the above two plethora of complex
  networks

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Phonological Neighborhood Network

• (Vitevitch 2004)
• (Gruenenfelder Pisoni, 2005)
• (Kapatsinski 2006)
• Sound Similarity Relations in the Mental
  Lexicon Modeling the Lexicon as a Complex
  Network

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N/W Definition

• Nodes Words
• Edge An edge is drawn from node A to node B if
  at least 2/3 of the segments that occur in word
  represented by A also occurs in the word
  represented by B
• i.e., if the word represented by A is 6 segments
  long then one can derive all its neighbors (B)
  from it by two phoneme changes (insertions,
  deletions or substitutions).

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N/W Construction

• Datbase
• Hoosier Mental Lexicon (Nusbaum et al., 1984)
• phonologically transcribed words ? n/w using the
  metric defined earlier
• Nodes with no links (correspond to hermit words
  i.e., words that have no neighbors)
• Random networks (E-R) for comparison
• Directed n/w ? a long word can have a short word
  as a neighbor, not vice versa
• Have a link only if the duration of the
  difference in the word pair lt (duration of a
  word)/3 (the factor 1/3 is experimentally derived
  see the paper for further info.)

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Neighborhood Density

• The node whose neighbors are searched ? base
  words
• Neighborhood density of a base word is expressed
  as the out-degree of the node representing the
  base word
• Is an estimate of the number of words activated
  by the base word when the base word is presented
  ? spreading activation
• Something like semantic priming (however, in the
  phonological level)

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Results of the N/W Analysis

• Small-world Properties
• High clustering but also long average path length
  -- like a SW network the lexicon has densely
  connected neighborhoods but the links between two
  nodes of different neighborhoods is harder to
  find than in SW networks

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Visualization A Disconnected Graph with a
Giant Component (GC)

• GC is elongated there are some nodes that have
  really long chain of intermediates and hence the
  mean path length is long

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Low Degree Nodes are Important!!!

• Removal of low degree nodes renders the n/w
  almost disconnected
• A bottleneck is formed between longer (more than
  7 segments long) and shorter words
• This bottleneck consists the tion final words
  coalition, passion, nation, fixation/fission
  they form short-cuts between the high-degree
  nodes (i.e., they are low-degree stars that
  connect mega-neighborhoods)

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Removal of Nodes with Degree lt 40
2-4 segment words
Removal of low-degree nodes disconnect the n/w
as opposed to the removal of hubs like pastor
(deg. 112)
8-10 segment words
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Why low connectivity between neighborhoods?

• Spreading activation should not inhibit
• neighbors of the stimulus neighbors that are
  non-neighbors of the stimulus itself (and are
  therefore, not similar to the stimulus)
• Low mean path ? complete traversal of n/ws, for
  e.g., in general purpose search
• Search in lexicon does not need to traverse links
  between distant nodes rather it involves an
  activation of the structured neighborhood that
  share a single sub-lexical chunk that could be
  acoustically related during word-recognition
  (Marslen-Wilson, 1990).

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Degree Distribution (DD)

• Exponential rather than power-law

5-7 segment words
Entire Lexicon
8-10 segment words
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Other Works (see supplementary material for
reference)

• Vitevitch (2005)
• similar to the above work but builds n/ws of
  nodes that are just one-segment different
• (Choudhury et al. (2007)
• Builds weighted n/ws in Hindi, Bengali and
  English based on orthographic proximity (nodes
  words edges orthographic edit-distance)
  SpellNet
• Does thresholding (?) to make the n/ws binary (at
  ? 1, 3, 5).
• They also obtain exponential DDs
• Observe that occurrence of real word errors in a
  language is proportional to avg. wghtd. deg. of
  the SpellNet of that language

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Other Works

• Sigman et al. (2002)
• Analyzes the English WordNet
• All semantic relationships are scale-invariant
• Inclusion of polysemy make the n/w SW
• Ferrer i Cancho et al. (2000,2001)
• Word co-occurrence (in a sentence) based
  definitions of the lexicon
• Lexicon Kernel Lexicon Peripheral Lexicon
• Finds a 2-regime DD one comprises words in the
  kernel lexicon and the other words in the
  peripheral lexicon
• Finds that these n/ws are small-world

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Some Unsolved Mysteries You can Give it a Try
?

• What can be a model for the evolution of the ML?
• How is the ML acquired by a child learner?
• Is there a single optimal structure for the ML
  or is it organized based on multiple criteria
  (i.e., a combination of the different n/ws)
  Towards a single framework for studying ML!!!