Difference between revisions of "Noun-Modifier Semantic Relations (State of the art)"
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Latest revision as of 05:16, 25 June 2012
- 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", "onion tear"
- Temporality: "morning frost", "summer travel"
- Spatial: "aquatic mammal", "west coast", "home remedy"
- Participant: "dream analysis", "mail sorter", "blood donor"
- Quality: "copper coin", "rice paper", "picture book"
Table of results
| Algorithm | Reference | 5-class F-measure | 5-class accuracy | 95% confidence for accuracy |
|---|---|---|---|---|
| Baseline | Majority class (Participant) | NA | 43.3% | 39.39-47.30% |
| VSM | Turney and Littman (2005) | 43.2% | 45.7% | 41.75-49.70% |
| SVM+28 | Nulty (2007) | NA | 50.1% | 46.11-54.09% |
| PERT | Turney (2006a) | 50.2% | 54.0% | 50.00-57.95% |
| TiMBL+WordNet | Nastase et al. (2006) | 51.5% | NA | NA |
| LRA | Turney (2006b) | 54.6% | 58.0% | 54.01-61.89% |
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
- 95% confidence for accuracy = confidence interval calculated using Wilson Test
- table rows sorted in order of increasing performance
- Baseline = always guess the majority class (Participant)
- VSM = Vector Space Model
- LRA = Latent Relational Analysis
- PERT = Pertinence
- TiMBL+WordNet = Tilburg Memory Based Learner + WordNet-based representation with word sense information
- SVM+28 = Support Vector Machine + all 28 joining terms
Other semantic relation test sets
- Diarmuid Ó Séaghdha: 1443 Compound Nouns
- SemEval 2007 Task 4: Classification of Semantic Relations between Nominals
- SemEval 2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
- SemEval 2010 Task 9: Noun Compound Interpretation Using Paraphrasing Verbs
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.
Nulty, Paul. (2007). Semantic classification of noun phrases using web counts and learning algorithms. In Proceedings of the ACL 2007 Student Research Workshop (ACL-07), pages 79-84. Prague, Czech Republic.
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.