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

Authors: Sánchez, Belém Priego | Pinto, David | Singh, Vivek Kumar

Article Type: Research Article

Abstract: A Verbal Phraseological Unit (VPU) is a phrase or expression that has either a literal or figurative (non-compositional) meaning. Identifying, in an automatic manner, which meaning is associated to a VPU in a certain context is an open problem in natural language processing, whose solution impacts in various real life applications. In this paper we present a method for the automatic verification of non-compositionality of a VPU based on the use of lexical domains. The methodology proposed is based in the degree of overlapping between the VPU domain and its context domain. This methdology is general in the sense that …it opens the opportunity of applying different overlapping metrics, and different methods for calculating the domain of a given phrase. The experiments carried out show that this novel approach can be efficiently applied in high volumes of data with competitive results close to 65%. Show more

Keywords: Idiom, disambiguation, lexical domain

DOI: 10.3233/JIFS-169490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3059-3067, 2018

Authors: Vicente, Marta | Barros, Cristina | Lloret, Elena

Article Type: Research Article

Abstract: This paper proposes an end-to-end Natural Language Generation approach to automatically create fiction stories using statistical language models. The proposed approach integrates the stages of macroplanning and the surface realisation, necessary to determine the content to write about together with the structure of the story, and the syntactic and lexical realisation of sentences to be generated, respectively. Moreover, the use of language models within the stages allows the generation task to be more flexible, as far as the adaptation of the approach to different languages, domains and textual genres is concerned. In order to validate our approach, two evaluations were …performed. On the one hand, the influence of integrating position-specific language modelling in the macroplanning stage into the surface realisation module was evaluated. On the other hand, a user evaluation was performed to analyse the generated stories in a qualitative manner. Although there is still room for improvement, the results obtained from the first evaluation in conjunction with the user evaluation feedback shows that the combination of the aforementioned stages in an end-to-end approach is appropriate and have positive effects in the resulting generated text. Show more

Keywords: Natural language generation, language modelling, document planning, surface realisation, automatic story generation

DOI: 10.3233/JIFS-169491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3069-3079, 2018

Authors: Mager, Manuel | Carrillo, Diónico | Meza, Ivan

Article Type: Research Article

Abstract: In this work, we present a morphological segmenter for the Mexican indigenous language Wixarika. Segmentation is fundamental for rich morphological languages, a common aspect of the native American languages, to improve other tasks like machine translation, dialogue systems, summarization, etc. On top of the agglutinative nature of the language, the low amount of resources and the lack of an orthographic standard among dialects add to the challenge. Our proposal is based on a probabilistic finite-state approach that exploits regular agglutinative patterns and requires little linguistic knowledge. We show that our approach outperforms unsupervised and semi-supervised methods in a low-resource context. …The dataset used in this work was openly released for future work by the community. Show more

Keywords: Morphology, low resources, finite-state transducer, Wixarika, endangered languages

DOI: 10.3233/JIFS-169492

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3081-3087, 2018

Authors: Ayala-Gómez, Frederick | Daróczy, Bálint | Benczúr, András | Mathioudakis, Michael | Gionis, Aristides

Article Type: Research Article

Abstract: Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation , i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata …is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs – and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10. Show more

Keywords: Citation recommendations, knowledge graphs, recommender systems

DOI: 10.3233/JIFS-169493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3089-3100, 2018

Authors: Piryani, Rajesh | Gupta, Vedika | Singh, Vivek Kumar | Pinto, David

Article Type: Research Article

Abstract: Books are an important source of knowledge to disseminate information. Researchers and academicians write books to propagate their innovative research or teachings amongst academic as well as non-academic audience. The number of books written every year is increasing rapidly. According to International Publisher Association (IPA) annual report 2015–2016, around 150 million different books were published worldwide in 2014–2015. Many e-commerce websites are also involved in selling books. A recent addition to book publishing world is e-books, which have really made it very simple to publish. While, availability of large number of books is good for readers, at the same time …it is challenging to find a good book, particularly in scholarly settings. Researchers in the area of Scientometrics have attempted to view assessment of goodness of a scholarly book by measuring citations that a book receive. However, citations alone are not a true measure of a book’s impact. Many a times people use the knowledge in a book without actually citing it. Also use of books in classroom settings or for general reading often is not reflected in terms of citations. Therefore, it is important to obtain users’s opinion about a book from other forms of data. Fortunately, we have now some data of this sort available in form of reviews, downloads and social media mentions etc. Amazon and Goodreads, both of which provide the readers’ views about a book, are two good examples. This paper presents an exploratory research work on using these non-traditional data about books to assess impact of a book. A set of Scopus-indexed computer science books with good citations as well as some other popular books in computer science domain are used for analysis. The reviews of books have been crawled in an automated fashion from Amazon and Goodreads. Thereafter sentiment analysis is carried out the text of reviews. Results of sentiment analysis are compared and correlated with traditional impact assessment metrics. The experimental analysis does not show a coherent relationship between citation and online reviews. Also, majority of the online reviews are found to be positive for large number of books in the dataset. As a related exercise, the Scopus citation data and Google scholar citation data for books are also compared. A high value of correlation is observed in these two. Overall the exploratory analysis provides a useful insight into the problem of book impact assessment. Show more

Keywords: Altmetrics, book impact, citation impact, review mining, sentiment analysis

DOI: 10.3233/JIFS-169494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3101-3110, 2018

Authors: Banshal, Sumit Kumar | Singh, Vivek Kumar | Kaderye, Golam | Muhuri, Pranab Kumar | Sánchez, Belém Priego

Article Type: Research Article

Abstract: Scholarly articles are considered one of the primary medium for dissemination of inventions and discoveries. Traditionally, usefulness and popularity of a scholarly article has been measured in terms of citations it receives. However, in the changed research publishing landscape, where most of the publications are now available in digital form accessible through various digital libraries; new measures of measuring usefulness of scholarly articles have emerged. Nowadays, scholarly articles are easily available for access and download from various digital access portals. The use and popularity of these digital access portals has also made it possible to integrate various social media platforms …with journal access and use. Most of the journals now maintain statistics about reads, number of downloads, social profile shares etc. Several newer platforms like ResearchGate, Academia and Mendeley have also become popular. Researchers now often share their articles on various such platforms and also use social media channels to disseminate their article to a wider audience. This transformed environment has allowed to track and measure usefulness and popularity of scholarly articles through alternative metrics (now popularly known as Altmetrics) as compared to traditional citation impact measures. Altmetrics attempts to derive impact of a scholarly article by using data from different kinds such as social network share, mentions, tweets etc. The use of Altmetrics varies widely from country to country and discipline to discipline. This paper attempts to present findings of an exploratory analysis of relevance of Altmetrics data through a case study of scholarly articles from India published during 2016 and indexed in Web of Science and also updated on ResearchGate. The results obtained provide an interesting insight on relatedness and correlation of presence of scholarly articles in Web of Science and ResearchGate. It is observed that about 61% papers indexed in Web of Science have an entry in ResearchGate. There are, however, disciplinary variations in presence of articles in ResearchGate. Only about 61% of the total disciplines in Web of Science are found to be covered in ResearchGate. Show more

Keywords: Scholarly articles, altmetrics, ResearchGate, social network analysis, Indian Research Output

DOI: 10.3233/JIFS-169495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3111-3118, 2018

Authors: García-Ramírez, Jesús | Olvera-López, J. Arturo | Olmos-Pineda, Ivan | Martín-Ortíz, Manuel

Article Type: Research Article

Abstract: Facial Expression Recognition (FER) is a research area that has been interesting for computer science community in recent years. In this paper, we propose a methodology for the three stages of a FER system. In the pre-processing stage a method based on edge detectors and thresholding operators for eyebrow and mouth segmentation is proposed; the next stage is feature extraction, we propose using polynomials as features for describing eyebrows and mouth regions. Finally, in classification stage different supervised learners such as: Neural Networks, K-Nearest Neighbors and C4.5 decision trees are tested in order to obtain a model for classifying three …out of six basic emotions (anger, happiness and surprise). According to our results, the proposed approach has acceptable accuracy for predicting new examples. Show more

Keywords: Expression recognition, face images pre-processing, supervised classification, interpolation features

DOI: 10.3233/JIFS-169496

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3119-3131, 2018

Authors: Álvarez-Carmona, Miguel A. | Pellegrin, Luis | Montes-y-Gómez, Manuel | Sánchez-Vega, Fernando | Escalante, Hugo Jair | López-Monroy, A. Pastor | Villaseñor-Pineda, Luis | Villatoro-Tello, Esaú

Article Type: Research Article

Abstract: The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gained interest in many problems related to computer forensics, psychology, marketing, but specially in those related with social media exploitation. As known, social media data is shared through a wide range of modalities (e.g., text, images and audio), representing valuable information to be exploited for extracting valuable insights from users. Nevertheless, most of the current work in AP using social media data has been devoted to analyze textual information only, …and there are very few works that have started exploring the gender identification using visual information. Contrastingly, this paper focuses in exploiting the visual modality to perform both age and gender identification in social media, specifically in Twitter. Our goal is to evaluate the pertinence of using visual information in solving the AP task. Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating posted images from all the users, making a distinction between tweeted and retweeted images. Performed experiments provide interesting evidence on the usefulness of visual information in comparison with traditional textual representations for the AP task. Show more

Keywords: Visual author profiling, age identification, gender identification, social media, Twitter, CNN representation

DOI: 10.3233/JIFS-169497

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3133-3145, 2018

Authors: Lara-Álvarez, Carlos | Reyes, Tania | Rodríguez-Rangel, Hector

Article Type: Research Article

Abstract: Counting the number of words and lines that a user reads is important for many educational purposes – e.g., the reading speed is a key factor to improve learning, intelligent systems can suggest text that must be read to achieve a determined learning objective. The eye tracking technology is commonly used to analyze the user reading habits. Counting the number of read words could be hard when the readings are obtained from imprecise eye tracking data – e.g., eye tracking calibration difficulties. Approaches that find patterns from saccades and fixations usually fail to solve the problem in such conditions. This …paper introduces the Cowl approach, which deals with the imprecision problem by associating the eye tracking data with points obtained from character recognition. To detect text lines truly read, the problem is stated as one of merging two hypothetical lines and it is solved by a Bayesian approach. Tests show that the proposed approach shows high performance, reaching average precision rates up to 0.866 for recall 0.976 – in the case of text with different orientations. Show more

Keywords: Eye tracking, multiple lines fitting, human computer interaction, line features

DOI: 10.3233/JIFS-169498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3147-3154, 2018

Authors: De Ita, Guillermo | Marcial-Romero, Raymundo | Bello, Pedro | Contreras, Meliza

Article Type: Research Article

Abstract: Let K be a knowledge base (KB) and let φ be new information, both propositional formulas expressed in conjunctive form (CF). We propose a deterministic and correct algorithm for performing the belief revision of φ in K , denoted as: K  ∘ φ . Our proposal satisfies subsets of AGM and KM postulates. We also present the soundness proof of our belief revision method, and the analysis of its time complexity.

Keywords: Belief revision, propositional inference, AGM postulates, KM postulates

DOI: 10.3233/JIFS-169499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3155-3164, 2018