Abstract: The new product attributes widely exist in the newly marketed products because of the application of new materials, new technologies and new processes. The existing product attributes extraction methods mainly focus on the extraction of core attributes, and new attributes are not recognized. This will affect the experimental results of related research based on attribute extraction. In view of this situation, we transformed the new attributes recognition into classification tasks, and utilized the classification model, conditional random field (CRF) and deep learning model (Bi-LSTM-CRF) to solve this task. We analyzed the experimental results, and decided to employ CRF model to get candidate new attributes. And we filtered noise by four strong rule-based methods. In order to enhance the interpretability of the new attributes, the new attributes were clustered through the idea of hierarchical clustering. Experimental results show that the proposed scheme of new attributes recognition can effectively extend the collection of product attributes.

Key words: New attributes extraction, Attributes clustering, CRF, Bi-LSTM-CRF