Recently, the task of distantly supervised (DS) ultra-fine entity typing has
received significant attention. However, DS data is noisy and often suffers
from missing or wrong labeling issues resulting in low precision and low
recall. This paper proposes a novel ultra-fine entity typing model with
denoising capability. Specifically, we build a noise model to estimate the
unknown labeling noise distribution over input contexts and noisy type labels.
With the noise model, more trustworthy labels can be recovered by subtracting
the estimated noise from the input. Furthermore, we propose an entity typing
model, which adopts a bi-encoder architecture, is trained on the denoised data.
Finally, the noise model and entity typing model are trained iteratively to
enhance each other. We conduct extensive experiments on the Ultra-Fine entity
typing dataset as well as OntoNotes dataset and demonstrate that our approach
significantly outperforms other baseline methods.

本文提出一种新颖的超细实体类型模型，其具有去噪能力，通过建立一个噪声模型对输入的上下文和实体类型标签进行估计，从输入中减去估计噪声以恢复更可靠的标签，并采用双编码器结构训练去噪数据，噪声模型和实体类型模型迭代训练，以提高彼此的性能，在 Ultra-Fine 实体类型数据集以及 OntoNotes 数据集上表现显著优于其他基线方法。

去噪增强远程监督的超细实体类型识别

Denoising Enhanced Distantly Supervised Ultrafine Entity Typing

Connections between relations in relation extraction, which we call class
ties, are common. In distantly supervised scenario, one entity tuple may have
multiple relation facts. Exploiting class ties between relations of one entity
tuple will be promising for distantly supervised relation extraction. However,
previous models are not effective or ignore to model this property. In this
work, to effectively leverage class ties, we propose to make joint relation
extraction with a unified model that integrates convolutional neural network
(CNN) with a general pairwise ranking framework, in which three novel ranking
loss functions are introduced. Additionally, an effective method is presented
to relieve the severe class imbalance problem from NR (not relation) for model
training. Experiments on a widely used dataset show that leveraging class ties
will enhance extraction and demonstrate the effectiveness of our model to learn
class ties. Our model outperforms the baselines significantly, achieving
state-of-the-art performance.

本文提出了一种基于卷积神经网络和普通成对排名框架的统一模型，引入三种新的排名损失函数和有效的方法来解决模型训练中的类别不平衡问题，成功地利用关系提取中的类别关系提高了抽取效果，并表明了模型学习类别关系的有效性和优越性。

通过有效的深度排序共同提取带类别关系的实体关系

Jointly Extracting Relations with Class Ties via Effective Deep Ranking

Distantly supervised relation extraction has been widely used to find novel
relational facts from plain text. To predict the relation between a pair of two
target entities, existing methods solely rely on those direct sentences
containing both entities. In fact, there are also many sentences containing
only one of the target entities, which provide rich and useful information for
relation extraction. To address this issue, we build inference chains between
two target entities via intermediate entities, and propose a path-based neural
relation extraction model to encode the relational semantics from both direct
sentences and inference chains. Experimental results on real-world datasets
show that, our model can make full use of those sentences containing only one
target entity, and achieves significant and consistent improvements on relation
extraction as compared with baselines. The source code of this paper can be
obtained from https: //github.com/thunlp/PathNRE.

本文提出了一种基于推理链和神经网络模型的远程监督关系抽取方法，能够更好地利用包含目标实体之一的句子，并在真实数据集上取得显著的抽取性能提升。