[Purpose/significance] Data science is emerging as a new interdisciplinary field which combines many fields. Extracting the corresponding entities knowledge from the announcement information of data science recruitment can not only help to understand the development of data science from a market perspective, but also help to improve the content of data science teaching.[Method/process] Based on the recruitment announcement from the recruitment website, combining with information science data collection, annotation and organization methods, data science corpus was constructed and the corresponding entities from it were extracted.[Result/conclusion] In the existing 11000 annotated data science corpus scale recruitment announcement, based on the Bi-LSTM-CRF, CRF and Bi-LSTM models, this paper compared the extraction performance of data science recruiting entities and finally determined the final data science recruitment entities automatic extraction model, designed the data science recruitment entities automatic extraction platform, and built a data science recruitment entities network.
Wang Dongbo
,
Hu Haotian
,
Zhou Xin
,
Zhu Danhao
. Research of Automatic Extraction of Entities of Data Science Recruitment and Analysis Based on Deep Learning[J]. Library and Information Service, 2018
, 62(13)
: 64
-73
.
DOI: 10.13266/j.issn.0252-3116.2018.13.009
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