Yuridiana Alemán

The Soundex Phonetic Algorithm Revisited for SMS Text Representation

The growing use of information technologies such as mobile devices has had a major social and technological impact such as the growing use of Short Message Services (SMS), a communication system broadly used by cellular phone users. In 2011, it was estimated over 5.6 billion of mobile phones sending between 30 and 40 SMS at month. Hence the great importance of analyzing representation and normalization techniques for this kind of texts. In this paper we show an adaptation of the Soundex phonetic algorithm for representing SMS texts. We use the modified version of the Soundex algorithm for codifying SMS, and we evaluate the presented algorithm by measuring the similarity degree between two codified texts: one originally written in natural language, and the other one originally written in SMS “sub-language”. Our main contribution is basically an improvement of the Soundex algorithm which allows to raise the level of similarity between the texts in SMS and their corresponding text in English or Spanish language.

Forecast of air quality based on ozone by decision trees and neural networks

In this paper we explore models based on decision trees and neural networks models for predicting levels of ozone. We worked with a data set of the Atmospheric Monitoring System of Mexico City (SIMAT), which includes measurements hour by hour, between 2010 to 2011. The data come from of three meteorological stations: Pedregal, Tlalnepantla and Xalostoc in Mexico city. The data set includes 8 parameters: four chemical variables and four meteorological variables. Based on our results, its possible to predict ozone levels with these parameters, with an accuracy of 94.4%.

Hierarchical Clustering Analysis: The Best-Performing Approach at PAN 2017 Author Clustering Task.

The author clustering problem consists in grouping documents written by the same author so that each group corresponds to a different author. We described our approach to the author clustering task at PAN 2017, which resulted in the best-performing system at the aforementioned task. Our method performs a hierarchical clustering analysis using document features such as typed and untyped character n-grams, word n-grams, and stylometric features. We experimented with two feature representation methods, log-entropy model, and TF-IDF, while tuning minimum frequency threshold values to reduce the feature dimensionality. We identified the optimal number of different clusters (authors) dynamically for each collection using the Calinski Harabasz score. The implementation of our system is available open source (https://github.com/helenpy/clusterPAN2017).

A Class Validation Proposal of a Pedagogic Domain Ontology based on Clustering Analysis

The knowledge bases of the Web are fundamentally organized in ontologies in order to answer queries based on semantics. The ontologies learning process comprises three fundamental steps: creation of classes and relationships, population and evaluation. In this paper the focus includes the classes creation, by introducing a class validation proposal using clustering analysis. As case of study was selected a pedagogical domain, where a corpus was semi-automatically built, from articles written in Spanish published in Social Sciences. Moreover, a dictionary containing classes, concepts and synonyms was included in the experiments. Clustering analysis allowed to verify the concepts that the experts considered as the most important for the domain. For the case of study selected, the cluster analysis step reports clusters with the same instances that the clusters defined by the experts.