Product attribute value extraction plays an important role for many
real-world applications in e-Commerce such as product search and
recommendation. Previous methods treat it as a sequence labeling task that
needs more annotation for position of values in the product text. This limits
their application to real-world scenario in which only attribute values are
weakly-annotated for each product without their position. Moreover, these
methods only use product text (i.e., product title and description) and do not
consider the semantic connection between the multiple attribute values of a
given product and its text, which can help attribute value extraction. In this
paper, we reformulate this task as a multi-label classification task that can
be applied for real-world scenario in which only annotation of attribute values
is available to train models (i.e., annotation of positional information of
attribute values is not available). We propose a classification model with
semantic matching and negative label sampling for attribute value extraction.
Semantic matching aims to capture semantic interactions between attribute
values of a given product and its text. Negative label sampling aims to enhance
the model's ability of distinguishing similar values belonging to the same
attribute. Experimental results on three subsets of a large real-world
e-Commerce dataset demonstrate the effectiveness and superiority of our
proposed model.

本文将产品属性值的提取任务重新定义为一个多标签分类任务，并提出了一种带有语义匹配和负标签采样的分类模型，实验证明该模型在真实世界的电子商务数据集中表现出有效性和优越性。

AE-smnsMLC：产品属性值提取的带语义匹配和负标签抽样的多标记分类

AE-smnsMLC: Multi-Label Classification with Semantic Matching and  Negative Label Sampling for Product Attribute Value Extraction

Document-level relation extraction (DocRE) predicts relations for entity
pairs that rely on long-range context-dependent reasoning in a document. As a
typical multi-label classification problem, DocRE faces the challenge of
effectively distinguishing a small set of positive relations from the majority
of negative ones. This challenge becomes even more difficult to overcome when
there exists a significant number of annotation errors in the dataset. In this
work, we aim to achieve better integration of both the discriminability and
robustness for the DocRE problem. Specifically, we first design an effective
loss function to endow high discriminability to both probabilistic outputs and
internal representations. We innovatively customize entropy minimization and
supervised contrastive learning for the challenging multi-label and long-tailed
learning problems. To ameliorate the impact of label errors, we equipped our
method with a novel negative label sampling strategy to strengthen the model
robustness. In addition, we introduce two new data regimes to mimic more
realistic scenarios with annotation errors and evaluate our sampling strategy.
Experimental results verify the effectiveness of each component and show that
our method achieves new state-of-the-art results on the DocRED dataset, its
recently cleaned version, Re-DocRED, and the proposed data regimes.

本文针对文档级关系抽取问题，设计一种具有高区分性和鲁棒性的方法，包括有效的损失函数，熵最小化和有监督对比学习，新颖的负标签采样策略，并在新的数据情境下进行评估。实验结果表明，该方法在 DocRED 数据集，Re-DocRED 和提出的数据情境下取得了最新的最优结果。