Journal of Intelligent & Fuzzy Systems - Volume 36, issue 5

Authors: Álvarez-Carmona, Miguel A. | Villatoro-Tello, Esaú | Montes-Y-Gómez, Manuel | Villaseñor-Pineda, Luis

Article Type: Research Article

Abstract:  Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features for solving this task. Thus, in this paper, we present a thorough analysis regarding the appropriateness of different distributional term representations (DTR) for the AP task. In this regard, we introduce a novel framework for supervised AP using these representations and, supported on it. We approach a comparative analysis of representations such …as DOR, TCOR, SSR, and word2vec in the AP problem. We also compare the performance of the DTRs against classic approaches including popular topic-based methods. The obtained results indicate that DTRs are suitable for solving the AP task in social media domains as they achieve competitive results while providing meaningful interpretability. Show more

Keywords: Author profiling, document representation, distributional term representation, text classification, social media

DOI: 10.3233/JIFS-179033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4857-4868, 2019

Authors: Posadas-Durán, Juan-Pablo | Gómez-Adorno, Helena | Sidorov, Grigori | Escobar, Jesús Jaime Moreno

Article Type: Research Article

Abstract:  We present a new resource to analyze and detect deceptive information that is present in a huge amount of news websites. Specifically, we compiled a corpus of news in the Spanish language extracted from several websites. The corpus is annotated with two labels (real and fake) for automatic fake news detection. Furthermore, the corpus also provides the category of the news, presenting a detailed analysis on vocabulary overlap among categories. Finally, we present a style-based fake news detection method. The obtained results show that the introduced corpus is an interesting resource for future research in this area.

Keywords: Fake news, corpus, Spanish, resource, machine learning

DOI: 10.3233/JIFS-179034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4869-4876, 2019

Authors: Guzmán-Cabrera, Rafael | Sánchez, Belém Priego | Mukhopadhyay, T. Prasad | García, J.M. Lozano | Cordova-Fraga, T.

Article Type: Research Article

Abstract:  It is increasingly common for internet users to have access to blogs and social networks, and common for them to express opinions on such sites. This research work is framed within the scope of opinion mining. Opinions allow us to measure people’s perception of a specific topic or product. Knowing the opinion that a person has towards a product or service is of great help for decision making, since it allows, between other things, that potential consumers to verify the quality of the product or service before using it. This research work is framed within the scope of opinion mining. …When the number of opinions is very large the analysis gets more complicated and generally resort to tools that allow this task to be performed automatically are sought out. The present work performs an automatic categorization of textual opinions corresponding to four products: books, DVDs, kitchens, and electronics. Both negative and positive opinions are considered for the experiment. Further categorization experiments are performed using different domains of learning. The basic idea is to investigate if we can undertake classification of opinions, positive and negative, of any given domain using instances of training from a different domain. Results obtained from different methods of learning are presented. The results obtained allow us to examine the feasibility of the proposed methodology. Show more

Keywords: Cross Domain, emotive classification, opinion classification

DOI: 10.3233/JIFS-179035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4877-4887, 2019

Authors: Sidorov, Grigori | Markov, Ilia | Kolesnikova, Olga | Chanona-Hernández, Liliana

Article Type: Research Article

Abstract: In spite of having been investigated for over fifty years, developing a robust spoken dialog management system remains an open research issue in robotics and natural language processing. In this paper, we present a language-independent spoken dialog management module integrated into a human-robot interaction system. We adopt an algorithmic approach to dialog modeling. A mobile robot functioning as a shopping assistant exemplifies the proposed approach. The dialog module is composed of a state transition network, in which state switches are conditioned by both visual and communicative factors. We use the formalism of a finite state automaton, where the robot changes …its state by performing a speech act or a non-verbal action from the set of specified act/action types. Show more

Keywords: Shopping assistant robot, spoken dialog management, speech acts, state transition network, finite automaton, visual factors, communicative factors

DOI: 10.3233/JIFS-179036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4889-4899, 2019

Authors: López-Ramírez, Pablo | Molina-Villegas, Alejandro | Siordia, Oscar S.

Article Type: Research Article

Abstract:  In this paper we propose an aggregation strategy for geolocated Twitter posts based on a hierarchical definition of the regular activity patterns within a specific region. The aggregation yields a series of documents that are used to train a topic model. The resulting model is tested against the ones produced by two other aggregation strategies proposed in the literature: aggregation by user and by hashtag . For comparison, we use quality metrics widely used on the literature. The results show that the Geographical Aggregation performs similarly to hashtag aggregation in terms of Jensen-Shannon Divergence and outperforms other aggregation schemes …in its ability to reproduce the original cluster labels. One potential application behind this is the discovery of unusual events or as a basis for geolocating messages from text. Show more

Keywords: Probabilistic topic modeling, geolocation, social network

DOI: 10.3233/JIFS-179037

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4901-4908, 2019

Authors: Basak, Rohini | Naskar, Sudip Kumar | Gelbukh, Alexander

Article Type: Research Article

Abstract: Given a question, a reference answer, and the answer given by the student, the aim of the automatic short answer grading task is to assign a grade to the student’s answer. We use for this a large number of matching rules relying on recognizing entailment relation between dependency structures of the two answers. Comparison of the grades generated by our method with those given by human judges on a computer science dataset shows a quite promising maximum correlation of 0.627.

Keywords: Automatic short answer grading, recognizing textual entailment, dependency parsing, semantic similarity

DOI: 10.3233/JIFS-179038

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4909-4919, 2019

Authors: Mager, Manuel | Rosales, Mónica Jasso | Çetinoğlu, Özlem | Meza, Ivan

Article Type: Research Article

Abstract:  User generated data in social networks is often not written in its standard form. This kind of text can lead to large dispersion in the datasets and can lead to inconsistent data. Therefore, normalization of such kind of texts is a crucial preprocessing step for common Natural Language Processing tools. In this paper we explore the state-of-the-art of the machine translation approach to normalize text under low-resource conditions. We also propose an auxiliary task for the sequence-to-sequence (seq2seq) neural architecture novel to the text normalization task, that improves the base seq2seq model up to 5%. This increase of performance closes …the gap between statistical machine translation approaches and neural ones for low-resource text normalization. Show more

Keywords: Noisy text, normalization, recurrent neural networks, low-resource, autoencoding

DOI: 10.3233/JIFS-179039

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4921-4929, 2019

Authors: Rodríguez-González, Ansel Y. | Martínez-Trinidad, José F. | Carrasco-Ochoa, Jesús A. | Ruiz-Shulcloper, José | Alvarado-Mentado, Matías

Article Type: Research Article

Abstract: There are many problems were the objects under study are described by mixed data (numerical and non numerical features) and similarity functions different from the exact matching are usually employed to compare them. Some algorithms for mining frequent patterns allow the use of Boolean similarity functions different from exact matching. However, they do not allow the use of non Boolean similarity functions. Transforming a non Boolean similarity function into a Boolean one, and then applying the previous algorithms for mining frequent patterns, could lead to loss some patterns, and even more to generate some other patterns which indeed should not …be considered as frequent similar patterns. In this paper, we extend the similar frequent pattern mining by allowing the use of non Boolean similarity functions. Several properties for pruning the search space of frequent similar patterns and a data structure that allows computing the frequency of patterns candidates, are proposed. Also, three algorithms for mining frequent patterns using non Boolean similarity functions are proposed. Experimental results show the efficiency and efficacy of the algorithms. The proposed algorithms obtain better patterns for classification than those patterns obtained by traditional frequent pattern miners, and miners using Boolean similarity functions. Show more

Keywords: Data mining, frequent patterns, similarity functions, Mixed data

DOI: 10.3233/JIFS-179040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4931-4944, 2019

Authors: Rodriguez-Torres, Fredy | Carrasco-Ochoa, Jesús A. | Martínez-Trinidad, José Fco.

Article Type: Research Article

Abstract: In supervised classification if one of the classes has fewer objects than the other, we have a class imbalance problem. One of the most common solutions to address class imbalance problems is oversampling, and SMOTE is the most referenced and well-known oversampling method. However, SMOTE creates synthetic objects in a random way, therefore it produces a different result each time it is applied, and in practice the user has to apply SMOTE several times for choosing the best of all the generated balanced datasets. For this reason, in this paper, we present SMOTE-D, a deterministic version of SMOTE, and propose …new deterministic SMOTE-D-based versions of some of the most recent and successful SMOTE-based methods. In our experiments, we show that all proposed deterministic methods produce as good results as random methods but our proposals need to be applied just once. This is very important from a practical point of view since our proposals save time by avoiding multiple applications of them as SMOTE does and they provide one unique result. Show more

Keywords: Imbalanced datasets, oversampling, supervised classification

DOI: 10.3233/JIFS-179041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4945-4955, 2019

Authors: Martínez-López, Yoan | Madera, Julio | Rodríguez-González, Ansel Y. | Barigye, Stephen

Article Type: Research Article

Abstract:  Optimization algorithms are important in problems of pattern recognition and artificial intelligence, i.e., the image recognition, face recognition, data analysis, optical recognition, etc. Estimation distribution algorithms (EDAs ) is kind of optimization algorithms based on substituting the crossover and mutation operators of the Genetic Algorithms by the estimation and later sampling the probability distribution learned from the selected individuals. However, a weakness of these algorithms is the efficiency in terms of the number of evaluations of the fitness function. In this paper, a Cellular Gaussian Estimation Algorithm (CEGA ) for solving continuous optimization problems is proposed. CEGA is derived …from evidence-based learning of independence and decentralized schemes of local populations. The experimental results showed that the present proposal reduces the number of evaluations of the fitness function in the search for optimums, maintaining its effectiveness in comparison to other algorithms of state-of-art using the same benchmark of continuous functions. Show more

Keywords: Cellular EDA, learning, probabilistic graph model, Gaussian networks

DOI: 10.3233/JIFS-179042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4957-4967, 2019