Publication: Aligning Knowledge Base and Document Embedding Models using Regularized Multi-Task Learning
Aligning Knowledge Base and Document Embedding Models using Regularized Multi-Task Learning
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Baumgartner, M., Zhang, W., Paudel, B., Dell’Aglio, D., Chen, H., & Bernstein, A. (2018). Aligning Knowledge Base and Document Embedding Models using Regularized Multi-Task Learning (No. 11136). 11136, 21–37. https://doi.org/10.1007/978-3-030-00671-6_2
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Knowledge Bases (KBs) and textual documents contain rich and complementary information about real-world objects, as well as relations among them. While text documents describe entities in freeform, KBs organizes such information in a structured way. This makes these two information representation forms hard to compare and integrate, limiting the possibility to use them jointly to improve predictive and analytical tasks. In this article, we study this problem, and we propose KADE, a solution based on a regularized multi-task learning o
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Baumgartner, M., Zhang, W., Paudel, B., Dell’Aglio, D., Chen, H., & Bernstein, A. (2018). Aligning Knowledge Base and Document Embedding Models using Regularized Multi-Task Learning (No. 11136). 11136, 21–37. https://doi.org/10.1007/978-3-030-00671-6_2