Novel Approach for Detecting the Number of Columns of a Résumé

Yavuz Bali; Günce Keziban Orman; Sultan Nezihe Turhan

Makale detayları

Novel Approach for Detecting the Number of Columns of a Résumé

Yazarlar:

Yavuz Bali | Günce Keziban Orman | Sultan Nezihe Turhan

Yayıncı:

Gazi University

Ana Başlıklar:

Computing Applications in Life Sciences

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.127-153

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342778

pISSN:

2147-9542

Anahtar kelimeler:

Özet:

 In recruitment processes, manually reviewing résumés is a highly time-consuming job. In order to reduce the cost of these reviews, Information Extraction tasks have been introduced to extract the structure of the document and the personal information contained within. However, because there is no consensus on a standard structure of résumés, i.e., each résumé has its own distinctive layout, column numbers, or text properties, an accurate extraction process becomes highly challenging. This study addresses a part of this problem. We focus on the problem of estimating the number of columns in résumés, as we experience in the further processes that knowing the number of columns facilitates the separation of the main sections of the résumés, hence the analysis of the finer subsections. We employ the coordinates of the text blocks that build up a résumé. We hypothesize that the coordinates of the text blocks carry information on the number of columns. We define the problem in a clustering context. We proposed a novel clustering approaches dedicated to finding the number of columns in a résumé by the separation of the text block coordinates. The experiments are conducted on a dataset of the résumés of real applicants in two languages: Turkish and English. The results reveal that hybrid approaches that use the intermediate methods perform better than the individual methods. Furthermore, these findings could be extended to any unstructured textual data in any language and document format.

 In recruitment processes, manually reviewing résumés is a highly time-consuming job. In order to reduce the cost of these reviews, Information Extraction tasks have been introduced to extract the structure of the document and the personal information contained within. However, because there is no consensus on a standard structure of résumés, i.e., each résumé has its own distinctive layout, column numbers, or text properties, an accurate extraction process becomes highly challenging. This study addresses a part of this problem. We focus on the problem of estimating the number of columns in résumés, as we experience in the further processes that knowing the number of columns facilitates the separation of the main sections of the résumés, hence the analysis of the finer subsections. We employ the coordinates of the text blocks that build up a résumé. We hypothesize that the coordinates of the text blocks carry information on the number of columns. We define the problem in a clustering context. We proposed a novel clustering approaches dedicated to finding the number of columns in a résumé by the separation of the text block coordinates. The experiments are conducted on a dataset of the résumés of real applicants in two languages: Turkish and English. The results reveal that hybrid approaches that use the intermediate methods perform better than the individual methods. Furthermore, these findings could be extended to any unstructured textual data in any language and document format.

 In recruitment processes, manually reviewing résumés is a highly time-consuming job. In order to reduce the cost of these reviews, Information Extraction tasks have been introduced to extract the structure of the document and the personal information contained within. However, because there is no consensus on a standard structure of résumés, i.e., each résumé has its own distinctive layout, column numbers, or text properties, an accurate extraction process becomes highly challenging. This study addresses a part of this problem. We focus on the problem of estimating the number of columns in résumés, as we experience in the further processes that knowing the number of columns facilitates the separation of the main sections of the résumés, hence the analysis of the finer subsections. We employ the coordinates of the text blocks that build up a résumé. We hypothesize that the coordinates of the text blocks carry information on the number of columns. We define the problem in a clustering context. We proposed a novel clustering approaches dedicated to finding the number of columns in a résumé by the separation of the text block coordinates. The experiments are conducted on a dataset of the résumés of real applicants in two languages: Turkish and English. The results reveal that hybrid approaches that use the intermediate methods perform better than the individual methods. Furthermore, these findings could be extended to any unstructured textual data in any language and document format.

 In recruitment processes, manually reviewing résumés is a highly time-consuming job. In order to reduce the cost of these reviews, Information Extraction tasks have been introduced to extract the structure of the document and the personal information contained within. However, because there is no consensus on a standard structure of résumés, i.e., each résumé has its own distinctive layout, column numbers, or text properties, an accurate extraction process becomes highly challenging. This study addresses a part of this problem. We focus on the problem of estimating the number of columns in résumés, as we experience in the further processes that knowing the number of columns facilitates the separation of the main sections of the résumés, hence the analysis of the finer subsections. We employ the coordinates of the text blocks that build up a résumé. We hypothesize that the coordinates of the text blocks carry information on the number of columns. We define the problem in a clustering context. We proposed a novel clustering approaches dedicated to finding the number of columns in a résumé by the separation of the text block coordinates. The experiments are conducted on a dataset of the résumés of real applicants in two languages: Turkish and English. The results reveal that hybrid approaches that use the intermediate methods perform better than the individual methods. Furthermore, these findings could be extended to any unstructured textual data in any language and document format.

Makale detayları

Novel Approach for Detecting the Number of Columns of a Résumé

Yazarlar:

Yavuz Bali | Günce Keziban Orman | Sultan Nezihe Turhan

Yayıncı:

Gazi University

Ana Başlıklar:

Computing Applications in Life Sciences

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.127-153

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342778

pISSN:

2147-9542

Information Extraction

Résumé Parsing

Clustering

Text Processing

Text Coordinates

Sayı

Makale detayları

Novel Approach for Detecting the Number of Columns of a Résumé

Yazarlar: Yavuz Bali | Günce Keziban Orman | Sultan Nezihe Turhan

Yayıncı: Gazi University

Ana Başlıklar: Computing Applications in Life Sciences

Dergi: Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.127-153

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001342778

pISSN: 2147-9542

Information Extraction

Résumé Parsing

Clustering

Text Processing

Text Coordinates

Sayı

Yazarlar:

Yavuz Bali | Günce Keziban Orman | Sultan Nezihe Turhan

Yayıncı:

Gazi University

Ana Başlıklar:

Computing Applications in Life Sciences

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.127-153

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342778

pISSN:

2147-9542