Clustering Text: A Comparison Between Available Text Vectorization Techniques

Singh, Lovedeep

doi:10.1007/978-981-16-1249-7_3

Cited by 2 publications

(3 citation statements)

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“…The results they obtain show better performance when TF-IDF is used as a vectorization technique. With a view to comparing existing vectorisation techniques and recommending one of them, Singh et al [11] to compare existing vectorisation techniques, compare the performance of text clustering applied following two vectorization techniques TF-IDF and Doc2Vec. They perform their experiments on a set of tweets and obtain better results with TF-IDF as a vectorization technique.…”

Section: Vectorization Stage For Text Classification By Supervised Ma...mentioning

confidence: 99%

“…More explicitly, we search the corpus for keywords that will define the new space of representation of the job offers, and the coordinates of the offers in this new space are defined thanks to values that vary according to the vectorization technique used. Inspired by [11], the techniques used in this article are TF-IDF and Doc2Vec.…”

Section: Vectorizationmentioning

confidence: 99%

“…It is used to assign classes (profiles) to job offers. Inspired by [11] and [15] we used the implementation of naive bayes Gaussian available on scikit-learn 5 .…”

Section: Naïve Bayesmentioning

confidence: 99%

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Class-oriented document vectorization as a new vectorization paradigm: application to job recommender systems

Tatchum,

Nzeko'o,

Makembe

et al. 2023

Preprint

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Nowadays, job recommender systems are more useful in the fight against unemployment due to their strong presence in e-recruitment platforms that are becoming very popular. Most of the job recommender systems based on machine learning models use a vector representation of job offers based on keywords. However, these keywords are results of vectorization which is applied on a collection of documents where each one is a job offer. In this case, each keyword discriminates one job offer from another, whereas it can be preferable that each keyword discriminates one class from another. Our aims is to improve job recommendation to user profiles, by applying vectorization on a class-oriented collection of documents in order to obtain more useful keywords for job offer representation. In this context, each class-oriented document corresponds to a user profile. Experiments are done on three datasets (Monster, Nigam and Minajobs), using TF-IDF and Doc2Vec as vectorization techniques, Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Tranformers erchitecture (TFM) as machine learning models for top-N recommendation, and Precision, MAP and F1-score as evaluation metrics. Results show that, compared to classic job recommender systems, improvement rates can go up to 19\%, 22\% and 20\% for systems based on Naive Bayes, go up to 34\%, 37\% and 34\% for those based on Decision tree, go up to 33\%, 34\%, 34\% for those based on SVM, and go up to 29\%, 40\% and 33\% for those based on transformers architecture, respectively in the Monster, Nigam and Minajobs datasets.

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Section: Vectorization Stage For Text Classification By Supervised Ma...mentioning

confidence: 99%

Section: Vectorizationmentioning

confidence: 99%