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courses:rg:2011-report-parser [2012/09/27 11:09]
ufal
courses:rg:2011-report-parser [2012/09/27 11:22] (current)
ufal
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-====== Using a Wikipedia-based Semantic Relatedness Measure for Document Clustering ======+====== A Fast, Accurate, Non-Projective, Semantically-Enriched Parser ======
  
 written by Stephen Tratz and Eduard Hovy (Information Sciences Institute, University of Southern Carolina) written by Stephen Tratz and Eduard Hovy (Information Sciences Institute, University of Southern Carolina)
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           * they reduced the number of generic "dep/DEP" relation           * they reduced the number of generic "dep/DEP" relation
               * Stanford tags are hierarchical and "dep/DEP" is the top-most one               * Stanford tags are hierarchical and "dep/DEP" is the top-most one
-          * 1.3% of arcs are non-projective (out of 8.1% of all non-projective arcs) because of the following conversion (agreement can be a motivation for this, i.e. in Czech): +          * correcting of POS using the syntactic info + additional rules for specific word forms 
-            {{:courses:rg:dependency-conversion.png|}} +          * 1.3% of arcs are non-projective (out of 8.1% of all non-projective arcs) because of the following conversion (agreement can be a motivation for this, i.e. in Czech): {{:courses:rg:dependency-conversion.png|}} 
-            +      - additional changes and the final conversion from the intermediate output to a dependency structure 
 ==== Parser ==== ==== Parser ====
  
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 ==== Evaluation ==== ==== Evaluation ====
   * they make the same transformation as they did in Section 2   * they make the same transformation as they did in Section 2
 +==== Shallow semantic annotation ====
 +  * 4 optional modules
 +      * preposition sense disambiguation
 +      * noun compound interpretation
 +      * possesives interpretation
 +      * PropBank semantic role labelling
 +          * (Hajič et al., 2009) is not in the list of references
 +===== Conclusion =====
 +  * non-directional easy-first parsing
 +  * new features - Brown et al. clusters
 +  * fast and accurate
 +  * modified Penn converter
 +      * changes in 9.500 POS tags
 +      * labels copula, coordination
 +  * semantic info

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