AUTOMATED RESUME SCREENING AND CANDIDATE RANKING SYSTEM USING ADVANCED NATURAL LANGUAGE PROCESSING AND SCIKIT-LEARN

Abstract

ABSTRACT “AUTOMATED RESUME SCREENING AND CANDIDATE RANKING SYSTEM USING ADVANCED NATURAL LANGUAGE PROCESSING AND SCIKIT-LEARN” The present invention relates to an automated resume screening and candidate ranking system that employs advanced natural language processing (NLP) techniques and deep learning algorithms. This end-to-end system efficiently processes resumes and job descriptions, leveraging methodologies such as Word2Vec, TF-IDF, and BERT for semantic feature extraction. Key components include a resume parser, feature extractor, job description analyzer, matching model, and ranking model. The system automatically scores and ranks candidates based on their relevance to job roles, eliminating manual feature engineering. By utilizing a custom similarity metric and personalized resume embeddings, the system achieves over 80% accuracy in candidate ranking on large datasets, significantly improving the efficiency and effectiveness of the recruitment process while ensuring a comprehensive evaluation of candidate profiles. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of automated resume screening, and more particularly, the present invention relates to the automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] The conventional method of manually reviewing resumes is exceptionally laborious, demands a significant amount of time, and is susceptible to personal biases. Hiring professionals are required to assess numerous resumes for each job vacancy, which renders it nearly impossible to evaluate every candidate thoroughly. As a consequence, this frequently results in the rejection of candidates who might be well-suited for the position due to surface-level criteria. Current resume scanning tools depend on basic keyword comparisons and lack a genuine comprehension of the nuances within language, devoid of proper semantic understanding. This outdated approach hinders the recruitment process by missing out on the more profound qualifications and potential of applicants.
[0004] Current solutions rely on fairly rudimentary techniques like keyword search, Boolean rules, and basic classifiers. These have several limitations - they do not consider the contextual meaning of words, cannot comprehend complex linguistic nuances and relationships, and often require extensive manual feature engineering. The proposed invention overcomes these limitations through an end-to-end deep learning approach that automates the entire pipeline from raw text to final candidate rankings.
[0005] In light of the foregoing, there is a need for Automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0007] The principal object of the present invention is to overcome the disadvantages of the prior art by providing Automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn.
[0008] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that saves countless man-hours by automating resume screening.
[0009] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that provides unbiased, high-quality candidate rankings
[0010] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that handles volumes of resumes per job opening with meager screening time per resume.
[0011] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that comprehensively evaluates each candidate's profile and potential fit
[0012] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that eliminates manual data pre-processing and feature engineering
[0013] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that self-learns continuously to improve accuracy over time
[0014] Another object of the present invention is to provide an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn that easily adapts to different job roles by training on relevant data.
[0015] This system can have two main commercial applications: Start-up companies can use this free resume screening system version to free up human resources from the hiring department so that it can focus more on its overall growth. An upgraded, monetized version of this system, which utilizes Web-Application integration, can be used by large companies to help filter out potential candidates directly from the web via hiring sites like LinkedIn and Twitter.
[0016] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0017] The present invention relates to an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn.
[0018] The proposed invention comprehensively solves these problems through an end-to-end automated resume screening and candidate ranking system powered by natural language processing and deep learning algorithms. The system takes in resumes and job descriptions as input. It then leverages techniques like word embedding, entity recognition, TF-IDF vectors, and neural networks to extract semantic features from the unstructured text data. These features are used to train a sophisticated deep-learning model to score and rank candidates based on relevance to the job role. The model can process hundreds of resumes in seconds to create a filtered shortlist of the most promising applicants.
[0019] The key components are:
- a) Resume Parser: Extracts text and structured fields from resumes
- b) Feature Extractor: Analyses text using Word2Vec, Docx2txt, TF-IDF, etc., to extract keywords, skills, experience, etc.
- c) Job Description Analyser: Processes job description to identify required qualifications and skills
- d) Matching Model: Custom similarity metric to score resumes based on job relevance
- e) Ranking Model: Ranks candidates using match score, skills, experience, etc.
[0020] The invention is highly innovative in the following aspects:
- i) Employs NLP techniques like Word2Vec, Docx2txt, BERT, named entity recognition, and part-of-speech tagging to extract semantic insights from text.
- ii) Automated feature extraction eliminates the need for manual engineering.
- iii) Custom deep neural network architecture combining CNNs, RNNs, and self-attention layers provides highly accurate semantic matching capabilities.
- iv) Incorporates personalized resume embedding for each candidate to evaluate their complete profile.
- v) Achieves over 80% accuracy in ranking candidates on large real-world datasets.
[0021] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0022] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0023] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0024] Figure 1: Basic job hiring process.
[0025] Figure 2: Basic process for resume screening.
[0026] Figure 3: General structure of resume analysis.
[0027] Figure 4: Resume Ranking structure.
DETAILED DESCRIPTION OF THE INVENTION
[0028] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0029] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0030] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0031] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0032] The present invention relates to an automated resume screening and candidate ranking system using advanced natural language processing and scikit-learn.
[0033] The proposed invention comprehensively solves these problems through an end-to-end automated resume screening and candidate ranking system powered by natural language processing and deep learning algorithms. The system takes in resumes and job descriptions as input. It then leverages techniques like word embedding, entity recognition, TF-IDF vectors, and neural networks to extract semantic features from the unstructured text data. These features are used to train a sophisticated deep-learning model to score and rank candidates based on relevance to the job role. The model can process hundreds of resumes in seconds to create a filtered shortlist of the most promising applicants.
[0034] The key components are:
- a) Resume Parser: Extracts text and structured fields from resumes
- b) Feature Extractor: Analyses text using Word2Vec, Docx2txt, TF-IDF, etc., to extract keywords, skills, experience, etc.
- c) Job Description Analyser: Processes job description to identify required qualifications and skills
- d) Matching Model: Custom similarity metric to score resumes based on job relevance
- e) Ranking Model: Ranks candidates using match score, skills, experience, etc.
[0035] The invention is highly innovative in the following aspects:
- i) Employs NLP techniques like Word2Vec, Docx2txt, BERT, named entity recognition, and part-of-speech tagging to extract semantic insights from text.
- ii) Automated feature extraction eliminates the need for manual engineering.
- iii) Custom deep neural network architecture combining CNNs, RNNs, and self-attention layers provides highly accurate semantic matching capabilities.
- iv) Incorporates personalized resume embedding for each candidate to evaluate their complete profile.
- v) Achieves over 80% accuracy in ranking candidates on large real-world datasets.
[0036] The core novel aspects include:
- a) NLP techniques like BERT for rich language understanding.
- b) End-to-end deep neural architecture requires no manual feature extraction.
- c) Comprehensive candidate modeling using entire profile history and skills.
- d) Personalized resume embedding to handle variations in language and structure.
- e) Optimized matching layers for comparing resumes to job descriptions.
[0037] Some of the key features of the innovation are:
- Resume Parsing - Extracts structured information from unstructured resume text using NLP techniques
- Candidate Profile Encoder - This uses a BERT-based language model to create embedded vector representations that encode the candidate's entire profile.
- Job Description Encoder - Encodes job postings into numerical vectors using BERT-based embedding
- Matching Model - Custom similarity metric compares candidate and job description vectors using multi-layer perceptron architecture.
- Ranking Model - Scores and ranks candidates using similarity scores along with other metrics like experience, education, etc.
- Cosine similarity metric customization
[0038] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims.
, Claims:CLAIMS
We Claim:
1) An automated resume screening and candidate ranking system utilizing advanced natural language processing (NLP) techniques and deep learning algorithms, the system comprising:
- a resume parser for extracting structured information from unstructured resume text;
- a feature extractor that analyzes the extracted text using techniques such as Word2Vec, TF-IDF, and Docx2txt to derive keywords, skills, and experience;
- a job description analyzer for identifying required qualifications and skills from job postings;
- a matching model employing a custom similarity metric to score resumes based on their relevance to job descriptions; and
- a ranking model that ranks candidates based on match scores, skills, experience, and other relevant metrics.
2) The system as claimed in claim 1, wherein the resume parser extracts text and structured fields from resumes, enabling a comprehensive analysis of candidate information.
3) The system as claimed in claim 1, wherein the advanced NLP techniques, including named entity recognition and part-of-speech tagging, are utilized to extract semantic insights from resumes.
4) The system as claimed in claim 1, wherein the system utilizes a multi-layer perceptron architecture to compute a custom similarity metric for comparing candidate vectors to job description vectors.
5) A candidate profile encoder within the system of claim 1 that employs a BERT-based language model to create embedded vector representations of a candidate's entire profile, facilitating enhanced evaluation of candidates.

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