Difference between revisions of "Noun-Modifier Semantic Relations (State of the art)"
| Line 34: | Line 34: | ||
| 50.2% | | 50.2% | ||
| 54.0% | | 54.0% | ||
| + | |- | ||
| + | | TiMBL+WordNet | ||
| + | | Nastase et al. (2006) | ||
| + | | 51.5% | ||
| + | | NA | ||
|- | |- | ||
| LRA | | LRA | ||
| Line 53: | Line 58: | ||
* 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.