3,492 research outputs found
Open-Category Classification by Adversarial Sample Generation
In real-world classification tasks, it is difficult to collect training
samples from all possible categories of the environment. Therefore, when an
instance of an unseen class appears in the prediction stage, a robust
classifier should be able to tell that it is from an unseen class, instead of
classifying it to be any known category. In this paper, adopting the idea of
adversarial learning, we propose the ASG framework for open-category
classification. ASG generates positive and negative samples of seen categories
in the unsupervised manner via an adversarial learning strategy. With the
generated samples, ASG then learns to tell seen from unseen in the supervised
manner. Experiments performed on several datasets show the effectiveness of
ASG.Comment: Published in IJCAI 201
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world
setting benefits various tasks such as entity disambiguation or knowledge graph
canonicalization. Existing works either only utilize entity features, or rely
on structured annotations from a single piece of context where the entity is
mentioned. To leverage diverse contexts where entities are mentioned, in this
paper, we generalize the distributional hypothesis to a multi-context setting
and propose a synonym discovery framework that detects entity synonyms from
free-text corpora with considerations on effectiveness and robustness. As one
of the key components in synonym discovery, we introduce a neural network model
SYNONYMNET to determine whether or not two given entities are synonym with each
other. Instead of using entities features, SYNONYMNET makes use of multiple
pieces of contexts in which the entity is mentioned, and compares the
context-level similarity via a bilateral matching schema. Experimental results
demonstrate that the proposed model is able to detect synonym sets that are not
observed during training on both generic and domain-specific datasets:
Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in
terms of Area Under the Curve and 3.19% in terms of Mean Average Precision
compared to the best baseline method.Comment: In IJCAI 2020 as a long paper. Code and data are available at
https://github.com/czhang99/SynonymNe
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