Convolutional Neural Network with Word Embedding Based Approach for Resume Classification

Nasser, Shabna; Sreejith, C.; Irshad, Muhammad

doi:10.1109/icetietr.2018.8529097

Cited by 16 publications

(2 citation statements)

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“…Several researchers have explored word embeddings for resume classification. Some notable and distinctive works in this direction are: -(a) GloVe word embeddings with convolutional neural networks [24] (b) Word2Vec word embeddings with Bidirectional LSTM with attention [25] (c) GloVe word embeddings with deep neural networks [26] (d) GloVe word embeddings with graph neural networks [27]. In [28], Zu and Wang claimed that the combination of Bidirectional LSTM with convolutional neural network and conditional random fields is the best classifier to classify word embedding sequences extracted from text blocks in a resume.…”

Section: Related Workmentioning

confidence: 99%

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

Susan,

Sharma,

Choudhary

2024

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In an increasingly competitive world, automated screening of resumes of candidates is the need of the hour given the large numbers of such resumes in career portals on the World Wide Web. Resume classification is a subset of the document classification problem in which the keywords extracted from the resume play a significant role in determining the job profile. In this paper, we explore the novel combination of uniqueness in terms of the number of occurrences of a keyword in a resume class as compared to the other resume classes, and the concept of semantics by representing the filtered keywords using word embeddings that can be used to find semantic similarities between resume documents. The principle of maximum entropy partitioning is used to find the keywords unique to a particular class. The aim is to use semantic representations of only those keywords that occur more frequently in one class more than in any other class; these are then passed as input to a Bidirectional long short-term memory (LSTM) for classification. Our experiments on a benchmark dataset proves that the proposed approach outperforms the state of the art in text classification by a significant margin proving the efficacy of our approach.

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Section: Related Workmentioning

confidence: 99%

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

Susan,

Sharma,

Choudhary

2024

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“…Recently, with the advancement of machine learning and natural language processing techniques, intelligent resume recommendation systems are being developed to facilitate the recruitment process and improve efficiency by more accurately identifying qualified resumes (Swami et al, 2022;Pal et al, 2022). Nasser et al (2018) proposed a resume classification model using a convolutional neural network with a word embedding model. Duan et al (2019) proposed a resume recommendation algorithm based on K-meansþþ and part-of-speech TF-IDF, showing a better performance than the word frequency vector.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based human resources recruitment system for business process management: using LSA, BERT and SVM

Tian

Pavur

Han

et al. 2022

BPMJ

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PurposeStudies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.Design/methodology/approachLSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.FindingsBased on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.Research limitations/implicationsThe technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.Practical implicationsThe novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.Originality/valueTo the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

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Data-Driven Resume Analysis Using Natural Language Processing and an Ensemble of Deep Learning

Mallo,

Odion,

Irhebhude

et al. 2024

Lecture Notes in Networks and Systems

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Convolutional Neural Network with Word Embedding Based Approach for Resume Classification

Cited by 16 publications

References 4 publications

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

A machine learning-based human resources recruitment system for business process management: using LSA, BERT and SVM

Data-Driven Resume Analysis Using Natural Language Processing and an Ensemble of Deep Learning

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