The process of evaluating resumes has evolved significantly over the years, with the advent of AI and Natural Language Processing (NLP) techniques playing a crucial role in automating and improving the efficiency of the process. TeamStation's research findings shed light on the potential of AI in revolutionizing the way resumes are evaluated and candidates are selected for various positions.
The Power of NLP and Text Mining
NLP and text mining techniques have been widely used to extract valuable information from resumes and identify knowledge profiles for software engineering positions[. These techniques help in automating the process of resume evaluation, making it more efficient and accurate. For instance, a resume evaluation system based on text mining was proposed by Chou et al., which demonstrated the potential of NLP in improving hiring practices.
Deep Learning for Feature Extraction
Feature extraction is a critical aspect of NLP, and deep learning techniques have been employed to improve the performance of NLP models in various tasks. Wang et al. presented a comprehensive analysis of feature extraction techniques in NLP for deep learning in the English language, emphasizing the significance of both traditional linguistic features and deep learning-based features.
Latent Dirichlet Allocation for Topic Modeling
Latent Dirichlet Allocation (LDA) is an unsupervised learning technique that has been used in resume parsing to identify underlying topics present in the resume content. LDA works by considering the text within a resume document as a collection of words and identifying the latent topics that are likely to generate those words. The goal of LDA is to uncover these latent topics and estimate the topic proportions for each resume.
Proposed Resume Rating System
The proposed resume rating system leverages LDA and Named Entity Recognition (NER) for rating and analysis of resumes. The combination of LDA for topic modeling and NER for entity extraction provides valuable insights into the themes, keywords, and important entities within the resumes, facilitating a more objective and informative assessment process.
The key steps involved in the proposed system include:
1. Data collection and text extraction: Gathering a diverse dataset of resumes and utilizing Spacy, a powerful NLP library, to accurately extract the textual content from resumes while preserving the document structure.
2. Topic modeling with LDA and keyword extraction: Applying the LDA model to the training data to identify latent topics and estimate the topic proportions for each resume.
3. Resume scoring: Generating a mathematical score as a metric of evaluation of one's resume based on a set of attributes derived from the resume itself.
The proposed system has achieved 87% accuracy with respect to only skills in consideration and an overall 92% accuracy with all attributes in consideration, such as college name, work experience, degree, and skills.
Final Analysis and what are we detecting
AI and NLP techniques have the potential to revolutionize the way resumes are evaluated and candidates are selected. TeamStation's research findings demonstrate the effectiveness of using LDA and NER for resume rating and analysis, providing valuable insights for recruiters and job seekers alike. As the field of AI continues to advance, we can expect even more sophisticated and accurate resume evaluation systems in the future.