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Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model
2017
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector Machines (SVMs) is promising as it can exploit both the syntactic patterns captured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work,
doi:10.18653/v1/d17-1093
dblp:conf/emnlp/TymoshenkoBM17
fatcat:aj3vtiqiurei3kcnfviuxw7hay