Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art concerning automatic document classification tasks relies on word and sentence embeddings built upon deep learning models based on transformers that have outperformed the competition in several tasks. However, the models built from these embeddings are usually difficult to interpret. On the contrary, linguistic features are easy to understand, they result in simpler models, and they usually achieve encouraging results. Moreover, both linguistic features and embeddings can be combined with different strategies which result in more reliable machine-learning models. The de facto tool for extracting linguistic features in Spanish is LIWC. However, this software does not consider specific linguistic phenomena of Spanish such as grammatical gender and lacks certain verb tenses. In order to solve these drawbacks, we have developed UMUTextStats, a linguistic extraction tool designed from scratch for Spanish. Furthermore, this tool has been validated to conduct different experiments in areas such as infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others. The results indicate that the combination of linguistic features and embeddings based on transformers are beneficial in automatic document classification.

In writing, humor is mainly based on figurative language in which words and expressions change their conventional meaning to refer to something without saying it directly. This flip in the meaning of the words prevents Natural Language Processing from revealing the real intention of a communication and, therefore, reduces the effectiveness of tasks such as Sentiment Analysis or Emotion Detection. In this manuscript we describe the participation of the UMUTeam in HaHackathon 2021, whose objective is to detect and rate humorous and controversial content. Our proposal is based on the combination of linguistic features with contextual and non-contextual word embeddings. We participate in all the proposed subtasks achieving our best result in the controversial humor subtask.