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Policy Texts with Topic Detection and Evolution Analysis

EasyChair Preprint no. 6632, version 2

Versions: 1234history
10 pagesDate: September 17, 2021


Talent policy has always been an important tool for countries to seize the talent highland and seek innovative development. However, talent policy text with complex theme, uneven distribution and unclear structure has caused great trouble for scholars to perform Talent Management. We constructed a large-scale unannotated cor- pus related to talent policy from Sougou Engine and collected 287 talent policies from local government in Guangdong Province, China, which has been fuelled by rapid mar- ket growth and an unprecedented population surge resulted in a labour force of record size. We proposed a novel clustering model called LDA2Vec, which merging LDA and Word2Vec, and performed topic evolution analysis in terms of topic similarity and topic entropy. The talent policies mainly included five topics: (i) talent introduction; (ii)talent training; (iii)talent guarantee; (iv)talent incentive, (v)talent evaluation. Talent policy in Guangdong province has gone through an evolutionary process from monism to pluralism. Taking the introduction of China's innovation-driven strategy in 2013 as the dividing line, it has direct ramifications for topic content and intensity of five topics.

Keyphrases: Lda2vec., Semantic Similarity., Talent management., Topic Entropy., Topic Evolution.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Li-Xia Chen and Guo-He Feng},
  title = {Policy Texts with Topic Detection and Evolution Analysis},
  howpublished = {EasyChair Preprint no. 6632},

  year = {EasyChair, 2021}}
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