Abstract:

Patent information extraction is the foundation of patent analysis, and its  attributes and attribute value extraction are important to patent information extraction. However, few studies focus on synchronously extracting attributes and their values in Chinese patent information extraction. Using abstracts of the Chinese patents as corpus, we propose a conditional random fields (CRFs) method based on statistic learning knowledge. Firstly,regarding the attributes and attribute values as named entities,we obtain a CRFs model by training sets, and then use this model to extract attributes and attribute values from the corpus.Secondly, we employ association rules to match the attributes and their values. Experimental results show that the accuracy, recall and Fscore can reach 80.8%, 81.2% and 81.0% respectively.The comparison of the extraction results proves the practical value of the proposal.

Key words: attribute extraction;attribute value extraction;Chinese patent;conditional random fields (CRFs)