  In this paper, we propose a method for resume rating using Latent Dirichlet
Allocation (LDA) and entity detection with SpaCy. The proposed method first
extracts relevant entities such as education, experience, and skills from the
resume using SpaCy's Named Entity Recognition (NER). The LDA model then uses
these entities to rate the resume by assigning topic probabilities to each
entity. Furthermore, we conduct a detailed analysis of the entity detection
using SpaCy's NER and report its evaluation metrics. Using LDA, our proposed
system breaks down resumes into latent topics and extracts meaningful semantic
representations. With a vision to define our resume score to be more
content-driven rather than a structure and keyword match driven, our model has
achieved 77% accuracy with respect to only skills in consideration and an
overall 82% accuracy with all attributes in consideration. (like college name,
work experience, degree and skills)
