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courses:rg:morphology-induction-with-spelling-rules [2011/04/11 14:43]
popel positive comment added 		courses:rg:morphology-induction-with-spelling-rules [2011/04/19 13:05] (current)
abzianidze
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 ===== Introduction ===== ===== Introduction =====
   * The paper describes morphology induction using Bayesian approach   * The paper describes morphology induction using Bayesian approach
 +    * It is based on the Minimum Description Length (MDL) principle
   * Baseline: Goldwater et al., 2006:  [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.9842&rep=rep1&type=pdf|Interpolating between types and tokens by estimation power-law generators]]   * Baseline: Goldwater et al., 2006:  [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.9842&rep=rep1&type=pdf|Interpolating between types and tokens by estimation power-law generators]]
     * only stem & suffix     * only stem & suffix
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   * Improvements to the baseline system   * Improvements to the baseline system
-    * introduces spelling rules (context/change, e.g.: "ut_i"/ε→t (in "shut.ing") or "ke_i"/e→ε (in "take.ing"))+    * introduces spelling rules (context/change, e.g.: "ut_i"/ε→t (in "shut.ing") or "ke_i"/e→ε (in "take.ing")), which are simultaneously learned with morphological analysis 
     * Dirichlet priors first set by hand: to prefer empty rules to deletion/insertion     * Dirichlet priors first set by hand: to prefer empty rules to deletion/insertion
    
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 ===== What do we like about the paper ===== ===== What do we like about the paper =====
   * Loganathan has the code (although he couldn't compile it, yet)   * Loganathan has the code (although he couldn't compile it, yet)
 +  * Spelling rules are also simultaneously learned along with morphological analysis 
   * It's unsupervised and clever: using just a couple of (hyper)parameters (some of them are learned automatically), they can describe a wide range of morphological rules - within one elegant framework.   * It's unsupervised and clever: using just a couple of (hyper)parameters (some of them are learned automatically), they can describe a wide range of morphological rules - within one elegant framework.

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