Journal of Intelligent & Fuzzy Systems - Volume 34, issue 5

Authors: Méndez-Molina, Arquímides | Oña-García, Ana Li | Carrasco-Ochoa, Jesús Ariel | Martínez-Trinidad, José Fco.

Article Type: Research Article

Abstract: Feature selection is a crucial aspect in classification problems, especially in domains such as text classification, where usually there is a large number of features. Recently, a two-stage feature selection method for text classification which combines class-based and corpus-based feature selection, was introduced. Based on their experiments, the authors conclude what parameter values for both, corpus-based and class-based approaches, allow a feature selection which improves the traditional methods in text classification. In this paper, we revisit this two-stage feature selection method and based on several experiments we come to a different conclusion: the parameters suggested by the original work do …not necessarily provide the best results. Based on our experiments, we conclude that by combining the best parameter value for each stage, for the specific corpus under study, the two stage selection method based on coverage policies provides a subset of features which allows to get statistically significant increase over the traditional methods in the success rates of the classifier. Show more

Keywords: Text classification, feature selection, parameter tunning

DOI: 10.3233/JIFS-169480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2949-2957, 2018

Authors: Banerjee, Somnath | Naskar, Sudip | Rosso, Paolo | Bandyopadhyay, Sivaji

Article Type: Research Article

Abstract: Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9 ] introduced the code-mixed cross-script question answering research problem and …reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions. Show more

Keywords: Question answering, code-mixing, cross-scripting, question classification, deep learning, social media content

DOI: 10.3233/JIFS-169481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2959-2969, 2018

Authors: dos Santos, Elder Donizetti | Quiles, Marcos Gonçalves | Faria, Fabio Augusto

Article Type: Research Article

Abstract: Online social networks like Instagram has more than 600 million users and creates over 300 million new posts every day. All those data can be used to detect real world events. Many works have been proposed in the literature to detect such events using different techniques, but this task is still hard. It involves many challenges including the processing of large volumes of data, the lack of a ground truth and the need for an adaptive approach. In this sense, our work attempts to tackle these problems with a semi-supervised learning approach to overcome those challenges using times series from …Instagram posts. Experimental studies demonstrate that similar time series can be used to generalize the knowledge and predict the occurrence of an event. Also, we demonstrate that Support Vector Regression is a good alternative to Gaussian Process Regression as the first provides good results using much less computing resources than the second. Moreover, we made our labeled dataset public, hoping it can be useful to other researchers as well. Show more

Keywords: Event detection, social networks, Instagram, Pearson correlation

DOI: 10.3233/JIFS-169482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2971-2982, 2018

Authors: Álvarez-Carmona, Miguel A. | Franco-Salvador, Marc | Villatoro-Tello, Esaú | Montes-y-Gómez, Manuel | Rosso, Paolo | Villaseñor-Pineda, Luis

Article Type: Research Article

Abstract: Paraphrase plagiarism identification represents a very complex task given that plagiarized texts are intentionally modified through several rewording techniques. Accordingly, this paper introduces two new measures for evaluating the relatedness of two given texts: a semantically-informed similarity measure and a semantically-informed edit distance. Both measures are able to extract semantic information from either an external resource or a distributed representation of words, resulting in informative features for training a supervised classifier for detecting paraphrase plagiarism. Obtained results indicate that the proposed metrics are consistently good in detecting different types of paraphrase plagiarism. In addition, results are very competitive against state-of-the …art methods having the advantage of representing a much more simple but equally effective solution. Show more

Keywords: Plagiarism identification, paraphrase plagiarism, semantic similarity, edit distance, Word2vec representation

DOI: 10.3233/JIFS-169483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2983-2990, 2018

Authors: Ramírez-de-la-Rosa, Gabriela | Villatoro-Tello, Esaú | Jiménez-Salazar, Héctor

Article Type: Research Article

Abstract: Resources such as labeled corpora are necessary to train automatic models within the natural language processing (NLP) field. Historically, a large number of resources regarding a broad number of problems are available mostly in English. One of such problems is known as Personality Identification where based on a psychological model (e.g. The Big Five Model), the goal is to find the traits of a subject’s personality given, for instance, a text written by the same subject. In this paper we introduce a new corpus in Spanish called Texts for Personality Identification (TxPI). This corpus will help to develop models to …automatically assign a personality trait to an author of a text document. Our corpus, TxPI-u, contains information of 416 Mexican undergraduate students with some demographics information such as, age, gender, and the academic program they are enrolled. Finally, as an additional contribution, we present a set of baselines to provide a comparison scheme for further research. Show more

Keywords: Language resource, Personality Identification, author profiling, natural language processing

DOI: 10.3233/JIFS-169484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2991-3001, 2018

Authors: Castillo, Esteban | Cervantes, Ofelia | Vilariño, Darnes

Article Type: Research Article

Abstract: This paper presents an approach to solve the author profiling, a text classification task, which consists in determining the demographic and psychological characteristics of an author (like age, gender and personality traits), from some samples of the author’s writing style. The main focus of the approach consists on the creation and enrichment of a co-occurrence graph using the link prediction theory in order to find an author’s profile considering a graph similarity technique (instead of a traditional supervised learning strategy). The proposed method is applied on the English language partition of the CLEF PAN 2015 author profiling task, producing competitive …results that are close to the best results reported so far, given the same training and test corpora. The experimental results show that the addition of new edges to a graph representation based on the topological neighborhood of words can be a valuable asset to infer and discover patterns in texts that comes from social media. Additionally, the use of a graph similarity provides a novel way for analyzing how alike are the texts related to a specific demographic or personality aspect against the writing style of an author. Show more

Keywords: Author profiling, supervised learning, co-occurrence graph, link prediction theory, graph similarity method

DOI: 10.3233/JIFS-169485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3003-3014, 2018

Authors: Bruzón, Adrian Fonseca | López-López, Aurelio | Medina Pagola, José E.

Article Type: Research Article

Abstract: Given the large volumes of information that are generated every day in the Web, Adaptive Information Filtering systems have the potential to become a very useful tool to handle such information overload. These systems allow users to focus on documents that actually meet their information needs, while the system discards the rest. Traditionally, these systems assume that terms of a document are not related to each other, and therefore their efficacy is limited. To overcome this limitation, we propose a method for extracting different relations between the terms of the documents that satisfy the information needs of the users, in …order to update the system modeling of such needs, and thereby improve its discrimination capability. These relations are based on the co-occurrence of terms at different levels of granularity, such as document, sentences or noun phrases. The experiments conducted indicate the potential of our proposal, which is capable of improving system efficacy, from the beginning and in the long run. Show more

Keywords: Terms relations, adaptive document filtering, user profile modeling, multi-level Contexts

DOI: 10.3233/JIFS-169486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3015-3026, 2018

Authors: Reyes-Ortiz, José A. | Bravo, Maricela

Article Type: Research Article

Abstract: There are several categories of criminal events. However, one of them is focused on people: criminal events against people. It directly affects some of the guarantee of a person or a family. These events are reported in digital media and, without neglecting, digital news media in Spanish. It is relevant to recognize criminal events against people to get useful information about the public security of citizens. Natural Language Processing has techniques that can be possible their identification. However, fine-grained linguistic analysis is required in order to carry out such task. This paper considers the enhancing the discovered patterns with linguistic …information (morphological and POS categories) to recognize criminal events against people from Spanish newspapers. Six categories of criminal events are considered: killing, violation, assault, suicide, kidnapping and sexual exploitation. An experimentation is carried out with a gold standard data set of criminal events. The experimentation shows promising results. Show more

Keywords: Criminal events, morphological and linguistic information, natural language processing, event recognition

DOI: 10.3233/JIFS-169487

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3027-3036, 2018

Authors: Garcia-Gorrostieta, Jesús Miguel | López-López, Aurelio

Article Type: Research Article

Abstract: Argumentation in academic writing is a challenging task required to communicate clear ideas. Exposed ideas have to be supported by reasoned arguments. Arguments are composed of components such as premises and conclusions. In this paper, we present an approach to classify argumentative components using language models and machine learning algorithms on a new corpus of academic theses and research proposals. We explore the use of lexical, syntactic, semantic and indicator features to tackle this task. We found that lexical features provide the best efficacy for the classification. For language models, the best features were syntactical. But our experiments showed that …a document occurrence representation with unigrams achieved the best accuracy. We also tested the conclusions about the representation and classifier on theses according to their study level (undergraduate, master, and doctoral). We analyzed the information gain of features and found patterns that are part of argumentative markers. Show more

Keywords: Computer-assisted argument analysis, academic writing, argumentation studies, argument components, annotated theses corpus

DOI: 10.3233/JIFS-169488

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3037-3047, 2018

Authors: Solovyev, Valery | Ivanov, Vladimir | Solnyshkina, Marina

Article Type: Research Article

Abstract: In this paper we explore to what extent text parameters, such as average number of words per sentence, syllables per word, nouns per sentence, frequency of content words, etc. can successfully rank Russian academic texts for different age and grade levels. We provide a brief overview of previous research on readability of Russian texts and describe the corpus of school textbooks on Social Studies (from 5-th to 11-th grade) compiled by the authors. We share our experience of using a variety of quantitative text complexity metrics and evaluate the measures of existing Russian text complexity formulas. Based on the tests …of a set of extended text features, we propose one innovative metric for better prediction of Russian text complexity, i.e. the number of adjectives. As the results obtained compare favorably with the previously published results on the established complexity metrics for Russian texts, the study encourages the development of valid, reliable and transparent complexity tools for Russian texts. Show more

Keywords: Text complexity, readability of academic text, Russian language, readability formulas

DOI: 10.3233/JIFS-169489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3049-3058, 2018