Difference between revisions of "Noun-Modifier Semantic Relations (State of the art)"

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| 50.2%
 
| 50.2%
 
| 54.0%
 
| 54.0%
 +
|-
 +
| TiMBL+WordNet
 +
| Nastase et al. (2006)
 +
| 51.5%
 +
| NA
 
|-
 
|-
 
| LRA
 
| LRA
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* LRA = Latent Relational Analysis
 
* LRA = Latent Relational Analysis
 
* PERT = Pertinence  
 
* PERT = Pertinence  
 +
* TiMBL+WordNet = Tilburg Memory Based Learner + WordNet-based representation with word sense information
  
  

Revision as of 16:37, 7 December 2007

  • 600 noun-modifier pairs labeled with 30 classes of semantic relations
  • 30 classes organized into 5 superclasses
  • introduced in Nastase and Szpakowicz (2003)
  • subsequently used by many other researchers
  • data available from UT Repository
  • information about data available from Vivi Nastase and Nastase and Szpakowicz (2003)


Five superclasses

  • Causality: "cold virus"
  • Temporality: "morning frost"
  • Spatial: "aquatic mammal"
  • Participant: "dream analysis"
  • Quality: "copper coin"


Table of results

Algorithm Reference 5-class F-measure 5-class accuracy
VSM Turney and Littman (2005) 43.2% 45.7%
PERT Turney (2006a) 50.2% 54.0%
TiMBL+WordNet Nastase et al. (2006) 51.5% NA
LRA Turney (2006b) 54.6% 58.0%


Explanation of table

  • Algorithm = name of algorithm
  • Reference = where to find out more about given algorithm and experiments
  • 5-class F-measure = macroaveraged F-measure for the 5 superclasses
  • 5-class accuracy = accuracy for the 5 superclasses
  • table rows sorted in order of increasing performance
  • VSM = Vector Space Model
  • LRA = Latent Relational Analysis
  • PERT = Pertinence
  • TiMBL+WordNet = Tilburg Memory Based Learner + WordNet-based representation with word sense information


References

Nastase, Vivi and Stan Szpakowicz. (2003). Exploring noun-modifier semantic relations. In Fifth International Workshop on Computational Semantics (IWCS-5), pages 285–301, Tilburg, The Netherlands.

Nastase, Vivi, Jelber Sayyad Shirabad, Marina Sokolova, and Stan Szpakowicz. (2006). Learning noun-modifier semantic relations with corpus-based and Wordnet-based features. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06), pages 781-787. Boston, Massachusetts.

Turney, Peter D. (2005). Measuring semantic similarity by latent relational analysis. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05), pages 1136–1141, Edinburgh, Scotland.

Turney, Peter D. and Michael L. Littman. (2005). Corpus-based learning of analogies and semantic relations. Machine Learning, 60(1–3):251–278.

Turney, P.D. (2006a). Expressing implicit semantic relations without supervision. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (Coling/ACL-06), Sydney, Australia, pp. 313-320.

Turney, P.D. (2006b). Similarity of semantic relations. Computational Linguistics, 32 (3), 379-416.


See also