Learning Alignments and Leveraging Natural Logic
(2007) Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing. (27.October.2023)
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- Abstract
- We describe an approach to textual inference that improves alignments at both the typed dependency level and at a deeper semantic level. We present a machine learning approach to alignment scoring, a stochastic search procedure, and a new tool that finds deeper semantic alignments, allowing rapid development of semantic features over the aligned graphs. Further, we describe a complementary semantic component based on natural logic, which shows an added gain of 3.13% accuracy on the RTE3 test set
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Citations
Chambers, N., Cer, D., Grenager, T., Hall, D., Kiddon, C., MacCartney, B., de Marneffe, M.-C., Ramage, D., Yeh, E., & Manning, C. D. (2007). Learning Alignments and Leveraging Natural Logic. Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing. Published. Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing. https://hdl.handle.net/2078.5/216272 (Original work published 2007)