3,233 research outputs found
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
We propose a distance supervised relation extraction approach for
long-tailed, imbalanced data which is prevalent in real-world settings. Here,
the challenge is to learn accurate "few-shot" models for classes existing at
the tail of the class distribution, for which little data is available.
Inspired by the rich semantic correlations between classes at the long tail and
those at the head, we take advantage of the knowledge from data-rich classes at
the head of the distribution to boost the performance of the data-poor classes
at the tail. First, we propose to leverage implicit relational knowledge among
class labels from knowledge graph embeddings and learn explicit relational
knowledge using graph convolution networks. Second, we integrate that
relational knowledge into relation extraction model by coarse-to-fine
knowledge-aware attention mechanism. We demonstrate our results for a
large-scale benchmark dataset which show that our approach significantly
outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of
70, 000 sentences on 100 relations derived from Wikipedia and annotated by
crowdworkers. The relation of each sentence is first recognized by distant
supervision methods, and then filtered by crowdworkers. We adapt the most
recent state-of-the-art few-shot learning methods for relation classification
and conduct a thorough evaluation of these methods. Empirical results show that
even the most competitive few-shot learning models struggle on this task,
especially as compared with humans. We also show that a range of different
reasoning skills are needed to solve our task. These results indicate that
few-shot relation classification remains an open problem and still requires
further research. Our detailed analysis points multiple directions for future
research. All details and resources about the dataset and baselines are
released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is
determined by dice rolling. Visit our website http://zhuhao.me/fewre
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the
instantiation parameters of a specific type of entity. In this paper, we
explore the capsule networks used for relation extraction in a multi-instance
multi-label learning framework and propose a novel neural approach based on
capsule networks with attention mechanisms. We evaluate our method with
different benchmarks, and it is demonstrated that our method improves the
precision of the predicted relations. Particularly, we show that capsule
networks improve multiple entity pairs relation extraction.Comment: To be published in EMNLP 201
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Distant supervision leverages knowledge bases to automatically label
instances, thus allowing us to train relation extractor without human
annotations. However, the generated training data typically contain massive
noise, and may result in poor performances with the vanilla supervised
learning. In this paper, we propose to conduct multi-instance learning with a
novel Cross-relation Cross-bag Selective Attention (CSA), which leads to
noise-robust training for distant supervised relation extractor. Specifically,
we employ the sentence-level selective attention to reduce the effect of noisy
or mismatched sentences, while the correlation among relations were captured to
improve the quality of attention weights. Moreover, instead of treating all
entity-pairs equally, we try to pay more attention to entity-pairs with a
higher quality. Similarly, we adopt the selective attention mechanism to
achieve this goal. Experiments with two types of relation extractor demonstrate
the superiority of the proposed approach over the state-of-the-art, while
further ablation studies verify our intuitions and demonstrate the
effectiveness of our proposed two techniques.Comment: AAAI 201
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