Distant Supervision for E-commerce Query Segmentation via Attention Network
This work addresses the need for accurate query segmentation in e-commerce platforms, but it is incremental as it builds on existing BiLSTM-CRF models with attention for a specific domain.
The paper tackles the problem of segmenting e-commerce queries by using distant supervision to leverage external documents, addressing data scarcity and Out-of-Vocabulary issues, and achieves improved performance on two datasets compared to baselines.
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.