SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings Chengyu Wang 1 , Xiaofeng He 1* , Aoying Zhou 2 1 School of Computer Science and Software Engineering, 2 School of Data Science and Engineering, East China Normal University Shanghai, China
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SphereRE: Distinguishing Lexical Relations with ... · Introduction (1) • Lexical Relation Classification – Task: Classifying a word pair into a finite set of relation types (e.g.,
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SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings
Chengyu Wang1, Xiaofeng He1*, Aoying Zhou2
1 School of Computer Science and Software Engineering,2 School of Data Science and Engineering,
• Learning 𝐽E– For each lexical relation type 𝑟. ∈ 𝑅
– Closed-form solution
• Approximating the probabilistic distribution over all lexical relation types 𝑅 w.r.t. (𝑥#, 𝑦#) ∈ 𝑈– Train a logistic regression classifier using the feature set
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SphereRE: Relation Representation Learning (1)
• Approximating 𝐽F– Learning a SphereRE vector 𝑟# for each (𝑥# , 𝑦#) ∈ 𝐷 ∪ 𝑈
– Re-writing 𝐽F via negative log likelihood
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Similar to node2vec!
SphereRE: Relation Representation Learning (2)
• Minimizing 𝐽FH by random walk based sampling– Sampling probability
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SphereRE: Relation Representation Learning (3)
• Overall Procedure of Learning SphereRE Vectors
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SphereRE: Lexical Relation Classification
• Train a feed-forward neural network over all the features to predict lexical relations
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Experiments (1)• Datasets and Experimental Settings