2017/05/29 by Hu Xu, Lei Shu, Xu, Hu +3 · 1 citation
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Topic Modeling #Web Data Mining and Analysis #cs.CL
paper · pdf · doi:10.48550/arxiv.1705.10030
arxiv created 2017/05/29 · openalex publication_date 2017/05/29 · arxiv updated 2017/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Extracting opinion targets is an important task in sentiment analysis on product reviews and complementary entities (products) are one important type of opinion targets that may work together with the reviewed product. In this paper, we address the problem of Complementary Entity Recognition (CER) as a supervised sequence labeling with the capability of expanding domain knowledge as key-value pairs from unlabeled reviews, by automatically learning and enhancing knowledge-based features. We use Conditional Random Field (CRF) as the base learner and augment CRF with knowledge-based features (called the Knowledge-based CRF or KCRF for short). We conduct experiments to show that KCRF effectively improves the performance of supervised CER task.