Distant Supervision for E-commerce Query Segmentation via Attention
  Network
  The booming online e-commerce platforms demand highly accurate approaches to
segment queries that carry the product requirements of consumers. Recent works
have shown that the supervised methods, especially those based on deep
learning, are attractive for achieving better performance on the problem of
query segmentation. However, the lack of labeled data is still a big challenge
for training a deep segmentation network, and the problem of Out-of-Vocabulary
(OOV) also adversely impacts the performance of query segmentation. Different
from query segmentation task in an open domain, e-commerce scenario can provide
external documents that are closely related to these queries. Thus, to deal
with the two challenges, we employ the idea of distant supervision and design a
novel method to find contexts in external documents and extract features from
these contexts. In this work, we propose a BiLSTM-CRF based model with an
attention module to encode external features, such that external contexts
information, which can be utilized naturally and effectively to help query
segmentation. Experiments on two datasets show the effectiveness of our
approach compared with several kinds of baselines.
