Job Offer Analysis Using Convolutional and Recurrent Convolutional Networks

Nowak, Jakub; Milkowska, Kamila; Scherer, Magdalena; Talun, Arkadiusz; Korytkowski, Marcin

doi:10.1007/978-3-030-61534-5_34

Cited by 1 publication

(1 citation statement)

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“…Jakub Nowak and Al. (2020) [13] address, using the supervised methods, the problem of non-uniformity of job names and descriptions by proposing two models: a convolutional network for text classification, consisting of six convolutional layers and three fully connected layers, and a recurrent network with long-term memory (LSTM) and Gated Recurrent Unit (GRU) cells with a convolutional input layer. In this solution, the description of the offer is entered word by word in the order in which it is written, this procedure simulates reading an ad on the Internet in the same way as humans.…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning Algorithm on Unstructured Documents for the Classification of Job Offers: Case of Cameroun

Makembe¹,

Etoundi²,

Tapamo³

2023

JCC

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Nowadays, in data science, supervised learning algorithms are frequently used to perform text classification. However, African textual data, in general, have been studied very little using these methods. This article notes the particularity of the data and measures the level of precision of predictions of naive Bayes algorithms, decision tree, and SVM (Support Vector Machine) on a corpus of computer jobs taken on the internet. This is due to the data imbalance problem in machine learning. However, this problem essentially focuses on the distribution of the number of documents in each class or subclass. Here, we delve deeper into the problem to the word count distribution in a set of documents. The results are compared with those obtained on a set of French IT offers. It appears that the precision of the classification varies between 88% and 90% for French offers against 67%, at most, for Cameroonian offers. The contribution of this study is twofold. Indeed, it clearly shows that, in a similar job category, job offers on the internet in Cameroon are more unstructured compared to those available in France, for example. Moreover, it makes it possible to emit a strong hypothesis according to which sets of texts having a symmetrical distribution of the number of words obtain better results with supervised learning algorithms.

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Section: Related Workmentioning

confidence: 99%