AE-smnsMLC: Multi-Label Classification with Semantic Matching and
  Negative Label Sampling for Product Attribute Value Extraction
Zhongfen Deng Wei-Te Chen Lei Chen Philip S. Yu
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.