A named entity recognition method towards product reviews based on BiLSTM-attention-CRF
Shunxiang Zhang, Haiyang Zhu, Hanqing Xu, Guangli Zhu, Kuan‐Ching Li · International Journal of Computational Science and Engineering · 2022
Named entity recognition (NER) towards product review intends to identify domain dependent named entities (e.g., organisation name, product name, etc.) from product reviews. Due to the fragmentation and non-construction of product reviews, traditional methods are difficult to capture the domain feature information and dependencies precisely. To solve the problem, we proposed a NER method towards product reviews based on BiLSTM-attention-CRF. Firstly, three kinds of features (character, word and part of speech) are integrated into the feature representation of texts. The final feature vector is obtained through training, mapping and linking the selected features. Then, the BiLSTM network is built to extract text features, and the attention mechanism is adopted to strengthen the capture of local features. Finally, CRF is applied to annotate and identify the entity. Compared with existing models, it is demonstrated that the proposed method can effectively recognise named entities from product reviews.