10 research outputs found
Multi-labeled Relation Extraction with Attentive Capsule Network
To disclose overlapped multiple relations from a sentence still keeps
challenging. Most current works in terms of neural models inconveniently
assuming that each sentence is explicitly mapped to a relation label, cannot
handle multiple relations properly as the overlapped features of the relations
are either ignored or very difficult to identify. To tackle with the new issue,
we propose a novel approach for multi-labeled relation extraction with capsule
network which acts considerably better than current convolutional or recurrent
net in identifying the highly overlapped relations within an individual
sentence. To better cluster the features and precisely extract the relations,
we further devise attention-based routing algorithm and sliding-margin loss
function, and embed them into our capsule network. The experimental results
show that the proposed approach can indeed extract the highly overlapped
features and achieve significant performance improvement for relation
extraction comparing to the state-of-the-art works.Comment: To be published in AAAI 201
Multi-Zone Unit for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely used to deal with sequence
learning problems. The input-dependent transition function, which folds new
observations into hidden states to sequentially construct fixed-length
representations of arbitrary-length sequences, plays a critical role in RNNs.
Based on single space composition, transition functions in existing RNNs often
have difficulty in capturing complicated long-range dependencies. In this
paper, we introduce a new Multi-zone Unit (MZU) for RNNs. The key idea is to
design a transition function that is capable of modeling multiple space
composition. The MZU consists of three components: zone generation, zone
composition, and zone aggregation. Experimental results on multiple datasets of
the character-level language modeling task and the aspect-based sentiment
analysis task demonstrate the superiority of the MZU.Comment: Accepted at AAAI 202
Object-Centric Learning with Capsule Networks : A Survey
The authors would like to thank all reviewers, and especially Professor Chris Williams from the School of Informatics of the University of Edinburgh, who provided constructive feedback and ideas on how to improve this work.Peer reviewe