Difference between revisions of "Noun-Modifier Semantic Relations (State of the art)"
Line 22: | Line 22: | ||
! Algorithm | ! Algorithm | ||
! Reference | ! Reference | ||
− | ! 5 class | + | ! 5-class F-measure |
− | ! 5 class accuracy | + | ! 5-class accuracy |
|- | |- | ||
| VSM | | VSM | ||
Line 29: | Line 29: | ||
| 43.2% | | 43.2% | ||
| 45.7% | | 45.7% | ||
+ | |- | ||
+ | | PERT | ||
+ | | Turney (2006a) | ||
+ | | 50.2% | ||
+ | | 54.0% | ||
|- | |- | ||
| LRA | | LRA | ||
− | | Turney ( | + | | Turney (2006b) |
| 54.6% | | 54.6% | ||
| 58.0% | | 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 | ||
Line 48: | Line 61: | ||
Turney, Peter D. and Michael L. Littman. (2005). [http://arxiv.org/abs/cs.LG/0508103 Corpus-based learning of analogies and semantic relations]. ''Machine Learning'', 60(1–3):251–278. | Turney, Peter D. and Michael L. Littman. (2005). [http://arxiv.org/abs/cs.LG/0508103 Corpus-based learning of analogies and semantic relations]. ''Machine Learning'', 60(1–3):251–278. | ||
− | Turney, P.D. ( | + | Turney, P.D. (2006a). [http://arxiv.org/abs/cs.CL/0607120 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. ( | + | Turney, P.D. (2006b). [http://arxiv.org/abs/cs.CL/0608100 Similarity of semantic relations]. ''Computational Linguistics'', 32 (3), 379-416. |
Revision as of 16:26, 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% |
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
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.