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End-to-End Reinforcement Learning for Automatic Taxonomy Induction
We present a novel end-to-end reinforcement learning approach to automat...
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Learning Concept Taxonomies from Multi-modal Data
We study the problem of automatically building hypernym taxonomies from ...
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Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings
We introduce the use of Poincaré embeddings to improve existing state-of...
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CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
Taxonomy is not only a fundamental form of knowledge representation, but...
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Fighting with the Sparsity of Synonymy Dictionaries
Graph-based synset induction methods, such as MaxMax and Watset, induce ...
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Event Representation with Sequential, Semi-Supervised Discrete Variables
Within the context of event modeling and understanding, we propose a new...
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Induction, of and by Probability
This paper examines some methods and ideas underlying the author's succe...
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Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary.
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