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--- projects:swe:start [2015/05/29 15:06] – hj
+++ projects:swe:start [2015/09/22 01:20] (current) – hj
@@ Line 3: / Line 3: @@
 ===== Semantic Word Embedding =====
-{{:projects:swe:fig-main-framework.png|?0x220 |SWE Framework}}
+{{:projects:swe:fig-main-framework.png?0x220|SWE Framework}}
 \\
-In this project, we propose a general framework
+In this project, we propose a general framework to incorporate semantic knowledge
-to incorporate semantic knowledge
+into the popular data-driven learning process of word embeddings to improve the
-into the popular data-driven learning process
+quality of them. Under this framework, we represent semantic knowledge as many
-of word embeddings to improve the
+**ordinal ranking inequalities** and formulate the learning of semantic word embeddings
-quality of them. Under this framework,
+(SWE) as a constrained optimization problem, where the data-derived objective
-we represent semantic knowledge as many
+function is optimized subject to all ordinal knowledge inequality constraints
-ordinal ranking inequalities and formulate
+extracted from available knowledge resources such as Thesaurus and Word-Net. We have demonstrated that this constrained optimization problem can be efficiently
-the learning of semantic word embeddings
+solved by the stochastic gradient descent (SGD) algorithm, even for a large
-(SWE) as a constrained optimization
-problem, where the data-derived objective
-function is optimized subject to all
-ordinal knowledge inequality constraints
-extracted from available knowledge resources
-such as Thesaurus and Word-
-Net. We have demonstrated that this constrained
-optimization problem can be efficiently
-solved by the stochastic gradient
-descent (SGD) algorithm, even for a large
 number of inequality constraints.
-\\
+**Project website:**
+  * Code and resource download at [[http://home.ustc.edu.cn/~quanliu|USTC]]  or [[ https://github.com/iunderstand/SWE|GitHub site]]
 **Reference:**
-\\
-[1] //Shiliang Zhang and Hui Jiang//, "Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks," [[http://arxiv.org/abs/1502.00702|arXiv:1502.00702]].
+[1] //Quan Liu, Hui Jiang, Si Wei, Zhen-Hua Ling and Yu Hu//, "Learning SemanticWord Embeddings based on Ordinal Knowledge Constraints," Proceedings of The 53th Annual Meeting of the Association for Computational Linguistics (ACL 2015), July, 2015. ([[https://aclweb.org/anthology/P/P15/P15-1145.pdf|here]])