关键词:

深度学习；命名实体识别；词袋模型；TF-IDF, “5W”规则

Abstract:

【Purpose/significance】 Extracting the target data needed by researchers from the massive academic literature content will help improve the research efficiency of researchers on the one hand, and improve the retrieval services of current literature databases on the other hand. 【Method/process】According to the academic needs of scientific researchers, first extract target data from a large number of academic documents through deep learning methods. Secondly, NER and TF-IDF are used to extract the "5W" rule of the target data, and then the target data is filtered by the second-level requirement rule. Any data that meets the "5W" rule is identified as the target data. Finally, the third layer of manual verification is performed on the target data, and the academic literature "target data" is finally generated.【Result/conclusion】The accuracy rate of the "target data" extraction model for academic literature constructed in this paper can reach 86.6%, and the integration of "5W" rule filtering and final manual verification will not only improve the academic literature search of scientific researchers Accuracy rate, and to a certain extent assist the retrieval work of literature database institutions.【Innovation/limitation】Combine the Deeping learning with rules of requirement to realize the search results of academic literature from the bibliographic information level of academic literature to the data level of the content of academic literature.

Key words:

Deep learning; Named entity recognition, Bag-of-words model, TF-IDF, "5W" rule