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Spherical Text Embedding
Yu Meng · Jiaxin Huang · Guangyuan Wang · Chao Zhang · Honglei Zhuang · Lance Kaplan · Jiawei Han

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #189

Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.

Author Information

Yu Meng (University of Illinois at Urbana-Champaign)
Jiaxin Huang (University of Illinois Urbana-Champaign)
Guangyuan Wang (UIUC)
Chao Zhang (Georgia Institute of Technology)
Honglei Zhuang (Google Research)
Lance Kaplan (U.S. Army Research Laboratory)
Jiawei Han (UIUC)

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