Author
Faruqui, Manaal and Dodge, Jesse and Jauhar, Sujay Kumar and Dyer, Chris and Hovy, Eduard and Smith, Noah A.
Conference
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Year
2015
Figures & Tables
Table 5: Spearman’s correlation for word similarity evaluation using the using original and retrofitted SG vectors.
Figure 2: Spearman’s correlation on the MEN word similarity task, before and after retrofitting.
Figure 1: Word graph with edges between related words showing the observed (grey) and the inferred (white)word vector representations.
Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement.
Table 4: Comparison of retrofitting for semantic enrichment against Yu and Dredze (2014), Xu et al. (2014). Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks.
Table 1: Approximate size of the graphs obtained from different lexicons.
Figure 3: Two-dimensional PCA projections of 100-dimensional SG vector pairs holding the “adjective to adverb”relation, before (left) and after (right) retrofitting.
Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type.
Table of Contents
- Abstract
- 1 Introduction
- 2 Retrofitting with Semantic Lexicons
- 3 Word Vector Representations
- and are of length 300. 1
- 4 Semantic Lexicons
- 5 Evaluation Benchmarks
- 6 Experiments
- 6.1 Retrofitting
- 6.2 Semantic Lexicons during Learning
- 6.3 Comparisons to Prior Work
- 6.4 Multilingual Evaluation
- 7 Further Analysis
- 8 Related Work
- 9 Conclusion
- Acknowledgements
- References
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