Jesus German Ortiz Barajas

2023

1. Job offers classifier using neural networks and oversampling methods

   Germán Ortiz, Gemma Bel Enguix, Helena Gómez-Adorno, and 2 more authors

   In Recent Developments and the New Directions of Research, Foundations, and Applications: Selected Papers of the 8th World Conference on Soft Computing, February 03–05, 2022, Baku, Azerbaijan, Vol. I, 2023

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   Both policy and research benefit from a better understanding of individuals’ jobs. However, as large-scale administrative records are increasingly employed to represent labour market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank in Mexico known as Bumeran5. We applied machine learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM). Using these algorithms, we trained multi-class models to classify job offers in one of the 23 classes (not uniformly distributed): Sales, Administration, Call Center, Technology, Trades, Human Resources, Logistics, Marketing, Health, Gastronomy, Financing, Secretary, Production, Engineering, Education, Design, Legal, Construction, Insurance, Communication, Management, Foreign Trade, and Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling algorithms to handle imbalanced classes. The proposed convolutional neural network architecture achieved the best results when applied the Geometric-SMOTE algorithm.

   @inproceedings{ortiz2023job,
     title = {Job offers classifier using neural networks and oversampling methods},
     author = {Ortiz, Germ{\'a}n and Enguix, Gemma Bel and G{\'o}mez-Adorno, Helena and Ameer, Iqra and Sidorov, Grigori},
     booktitle = {Recent Developments and the New Directions of Research, Foundations, and Applications: Selected Papers of the 8th World Conference on Soft Computing, February 03--05, 2022, Baku, Azerbaijan, Vol. I},
     pages = {235--248},
     year = {2023},
     organization = {Springer},
   }

2022

1. Sentence-CROBI: A Simple Cross-Bi-Encoder-Based Neural Network Architecture for Paraphrase Identification

   Jesus-German Ortiz-Barajas, Gemma Bel-Enguix, and Helena Gómez-Adorno

   Mathematics, 2022

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   Since the rise of Transformer networks and large language models, cross-encoders have become the dominant architecture for various Natural Language Processing tasks. When dealing with sentence pairs, they can exploit the relationships between those pairs. On the other hand, bi-encoders can obtain a vector given a single sentence and are used in tasks such as textual similarity or information retrieval due to their low computational cost; however, their performance is inferior to that of cross-encoders. In this paper, we present Sentence-CROBI, an architecture that combines cross-encoders and bi-encoders to obtain a global representation of sentence pairs. We evaluated the proposed architecture in the paraphrase identification task using the Microsoft Research Paraphrase Corpus, the Quora Question Pairs dataset, and the PAWS-Wiki dataset. Our model obtains competitive results compared with the state-of-the-art by using model ensembles and a simple model configuration. These results demonstrate that a simple architecture that combines sentence pair and single-sentence representations without using complex pre-training or fine-tuning algorithms is a viable alternative for sentence pair tasks.

   @article{ortiz2022sentence,
     author = {Ortiz-Barajas, Jesus-German and Bel-Enguix, Gemma and G{\'o}mez-Adorno, Helena},
     journal = {Mathematics},
     volume = {10},
     number = {19},
     pages = {3578},
     year = {2022},
     publisher = {MDPI},
   }

2019

1. Detection of Aggressive Tweets in Mexican Spanish Using Multiple Features with Parameter Optimization.

   Germán Ortiz, Helena Gómez-Adorno, Jorge Reyes-Magaña, and 2 more authors

   In IberLEF@ SEPLN, 2019

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   This paper explains our approach to Aggressiveness Identification in the MEX-A3T shared task, whose aim is the detection of aggressive tweets. The task proposes a binary classification for every tweet: aggressive and non-aggressive. We approached the problem using linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, among others). We trained a Support Vector Machine using a combinatorial framework that optimizes the results of the classifier. Our best run achieved an F1-score of 0,4549, which is the 5th best among the twenty-six runs.

   @inproceedings{ortiz2019detection,
     title = {Detection of Aggressive Tweets in Mexican Spanish Using Multiple Features with Parameter Optimization.},
     author = {Ortiz, Germ{\'a}n and G{\'o}mez-Adorno, Helena and Reyes-Maga{\~n}a, Jorge and Bel-Enguix, Gemma and Sierra, Gerardo},
     booktitle = {IberLEF@ SEPLN},
     pages = {520--525},
     year = {2019},
   }