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

Authors: Frenda, Simona | Ghanem, Bilal | Montes-y-Gómez, Manuel | Rosso, Paolo

Article Type: Research Article

Abstract:  Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results.

Keywords: Misogyny detection, sexism detection, linguistic analysis

DOI: 10.3233/JIFS-179023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4743-4752, 2019

Authors: Alemán, Yuridiana | Somodevilla, María J. | Vilariño, Darnes

Article Type: Research Article

Abstract: In this paper an analysis, based on similarity metrics, was carried out in order to detect main concepts related to the superclasses in a pedagogical domain ontology. A semi-automatic corpus containing articles in Spanish was built. Afterward, the corpus was lemmatized and three representations were extracted. Four textual similarity metrics based on terms and Pointwise Mutual Information were implemented. A list of words, which was evaluated using a gold standard built by an expert in the domain, was retrieved from each experiment according to establish thresholds for the metrics. Precision and recall were used for evaluation step, where a detailed …discussion by representation and class was presented. Results showed a higher precision in types of intelligences class and 5-grams representation. Show more

Keywords: Ontology learning, pedagogical domain, NLP.

DOI: 10.3233/JIFS-179024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4753-4764, 2019

Authors: Buitrón, Edwar Javier Girón | Corrales, David Camilo | Avelino, Jacques | Iglesias, Jose Antonio | Corrales, Juan Carlos

Article Type: Research Article

Abstract:  The coffee rust is a devastating disease that causes large economic losses across the world. The severity of this disease changes over time so the farmers are not fully aware of the economic importance of the rust disease in the coffee crops. From a computational science perspective, several investigations have been proposed to decrease the effects caused by the coffee rust appearance from Expert systems based on machine learning techniques. However, because samples about coffee rust incidence are few, the rules created from machine learning techniques do not contain enough information to consider the diversity of scenarios for detecting coffee …rust. This paper proposes an expert system based on rules, where the rules are created considering the expert knowledge of specialists and technical reports about the behavior of the disease during a crop year. As far as we know, this is the first expert system proposed using not only expert knowledge but also technical reports in the coffee rust problem. The Buchanan methodology is used to design the proposed system. Experiment results present an average accuracy of 66,67% to detect a correct warning of coffee rust levels. Show more

Keywords: Decision support system, crops, disease, agriculture, hemileia vastatrix

DOI: 10.3233/JIFS-179025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4765-4775, 2019

Authors: Lithgow-Serrano, Oscar | Collado-Vides, Julio

Article Type: Research Article

Abstract: The constant increase in the production of scientific literature is making it very difficult for experts to keep up to date with the state-of-the-art knowledge in their fields. The use of Natural Language Processing (NLP) is becoming a necessary aid to tackle this challenge. In the NLP field, the task of measuring semantic similarity between two sentences plays a vital role. It is a cornerstone for tasks like Q&A, Information Retrieval, Automatic Summarization, etc., and it is a crucial element in the ultimate goal of computers being able to decode what is conveyed in human language expression. Measuring Semantic …Similarity (SS) in short texts has specific challenges. Because there are fewer words to be compared, the meaning contribution of each word is more relevant, and it is important to take into account the syntax’s contribution to the composed meaning. In addition, the highly specific and specialized vocabulary — Microbial Transcriptional-Regulation—implies the lack of massive training resources. Our approach has been to use an ensemble of similarity metrics including string, distributional, and knowledge-based metric and to combine the results of such analyses. We have trained and tested these methods in a similarity corpus developed in-house. The task has proved very challenging, and the ensemble strategy has proved to be a good approach. Even though there is still much room for improvement in the precision of our methods concerning the human evaluation, we have managed to improve them reaching a strong correlation (ρ  = 0.700). Show more

Keywords: Natural Language Processing, Semantic Textual Similarity

DOI: 10.3233/JIFS-179026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4777-4786, 2019

Authors: Dash, Sandeep Kumar | Saha, Saurav | Pakray, Partha | Gelbukh, Alexander

Article Type: Research Article

Abstract:  Caption generation requires best of both Computer Vision and Natural Language Processing. Due to recent improvements in both of them many efficient models have been developed. Automatic Image Captioning can be utilized to provide descriptions of website content or to engender frame-by-frame descriptions of video for the vision-impaired and in many such applications. In this work, a model is described which is utilized to generate novel image captions for a previously unseen image by utilizing a multimodal architecture by amalgamation of a Recurrent Neural Network (RNN) and a Convolutional Neural Network (CNN). The model is trained on Microsoft Common Objects …in Context (MSCOCO), an image captioning dataset that aligns captions and images in the same representation space, so that an image is close to its relevant captions in that space and far away from dissimilar captions and dissimilar images. ResNet-50 architecture is used for extracting features from the images and GloVe embeddings are used along with Gated Recurrent Unit (GRU) in Recurrent Neural Network (RNN) for text representation. MSCOCO evaluation server is used for evaluation of the machine generated caption for a given image. Show more

Keywords: Image captioning, convolutional neural network

DOI: 10.3233/JIFS-179027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4787-4796, 2019

Authors: Majumder, Goutam | Pakray, Partha | Pinto, David

Article Type: Research Article

Abstract:  This work focuses on bolstering the pre–existing Interpretable Semantic Textual Similarity (iSTS) method, that will enable a user to understand the behaviour of an artificial intelligent system. The proposed iSTS method explains the similarities and differences between a pair of sentences. The objective of the iSTS problem is to formalize the alignment between a pair of text segments and to label the relationship between the text fragments with a relation type and relatedness score. The overall objective of this work is to develop a 1:M multi chunk aligner for an iSTS method, which is trained on SemEval 2016 Task …2 dataset. The obtained result outperforms many state–of–art aligners, which were part of SemEval 2016 iSTS task. Show more

Keywords: WordNet, interpretability, semantic semilarity, Natural Language Processing, cosine similarity

DOI: 10.3233/JIFS-179028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4797-4808, 2019

Authors: Srivastava, Jyoti | Sanyal, Sudip | Srivastava, Ashish Kumar

Article Type: Research Article

Abstract: Word reordering is an important problem for translation between languages which have different structures such as Subject-Verb-Object and Subject-Object-Verb. This paper presents a statistical method for extraction of linguistic rules using chunk to reorder the output of the baseline statistical machine translation system for improved performance. The experiments are based on the TDIL sample tourism corpus of English-Hindi language pair which consists of 1000 sentence pairs out of which 900 sentence pairs are used for training, 50 sentences for tuning and 50 sentences for testing. Finally, the output of the machine translation system, augmented by these rules, is evaluated by …using BLEU and NIST metrics. The BLEU score improves by more than 2% in comparison to the baseline SMT system. The results are compared with those of Google translation system which has been trained on a huge corpus. We got a 0.1 point improvement in terms of NIST score, in comparison to Google Translation. Thus, we have comparable results with such a small corpus of 900 sentence pairs for training. This paper is an effort to improve the performance of SMT with a small corpus by using linguistic rules where the rules are automatically generated instead of made by linguist. Show more

Keywords: Statistical machine translation, chunk, rule extraction, reordering rules, hybrid machine translation

DOI: 10.3233/JIFS-179029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4809-4819, 2019

Authors: Sengupta, Saptarshi | Pandit, Rajat | Mitra, Parag | Naskar, Sudip Kumar | Sardar, Mohini Mohan

Article Type: Research Article

Abstract: One of the most challenging research problems in natural language processing (NLP) is that of word sense induction (WSI). It involves discovering senses of a word given its contexts of usage without the use of a sense inventory which differentiates it from traditional word sense disambiguation (WSD). This paper reports a work on sense induction in Bengali, a less-resourced language, based on distributional semantics and translation based context vectors learned from parallel corpora to improve the task performance. The performance of the proposed method of sense induction was compared with the k-means algorithm, which was considered as the baseline in …our work. A dataset for sense induction was created for 15 Bengali words, encompassing a total of 111 contexts. The proposed model, in both mono and cross-lingual settings, outperformed k-means in precision (P), recall (R) and F-scores. K-means based sense induction produced average P, R and F-scores of 0.71, 0.73 and 0.66 respectively. The average P, R and F-scores produced by the mono-and cross-lingual settings of the proposed algorithm are 0.77, 0.73, 0.68 and 0.81, 0.77 and 0.72 respectively. Show more

Keywords: Word sense induction (WSI), parallel corpora, translation, Word2Vec, context clustering

DOI: 10.3233/JIFS-179030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4821-4832, 2019

Authors: Ameer, Iqra | Sidorov, Grigori | Nawab, Rao Muhammad Adeel

Article Type: Research Article

Abstract: The process of automatic identification of an author’s demographic traits like gender, age, native language, geographical location, personality type and others from his/her written text is termed as author profiling (AP). Currently, it has engaged the research community due to its promising uses in security, marketing, forensic, bogus account identification on public networks. A variety of benchmark corpora (English text) released by PAN shared task is used to perform our experiments. This study presents a Content-based approach for detection of author’s traits (age group and gender) for same-genre author profiles. In our proposed method, we used a different set of …features including syntactic n-grams of part-of-speech tags, traditional n-grams of part-of-speech tags, the combination of word n-grams and combination of character n-grams. We tried a range of classifier for several profile sizes. We used the word uni-grams and character tri-grams as our baseline approaches. We achieved best accuracy of 0.496 and 0.734 for both traits, i.e., age group and gender respectively, by applying the combination of word n-grams of various sizes. Experimental results signify that the combination of word n-grams can produce good results on benchmark corpora. Show more

Keywords: Author profiling, machine learning, syntactic n-grams, traditional n-grams, part-of-epeech

DOI: 10.3233/JIFS-179031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4833-4843, 2019

Authors: Gómez-Adorno, Helena | Fuentes-Alba, Roddy | Markov, Ilia | Sidorov, Grigori | Gelbukh, Alexander

Article Type: Research Article

Abstract: We present a method for gender and language variety identification using a convolutional neural network (CNN). We compare the performance of this method with a traditional machine learning algorithm – support vector machines (SVM) trained on character n-grams (n  = 3–8) and lexical features (unigrams and bigrams of words), and their combinations. We use a single multi-labeled corpus composed of news articles in different varieties of Spanish developed specifically for these tasks. We present a convolutional neural network trained on word- and sentence-level embeddings architecture that can be successfully applied to gender and language variety identification on a relatively small corpus …(less than 10,000 documents). Our experiments show that the deep learning approach outperforms a traditional machine learning approach on both tasks, when named entities are present in the corpus. However, when evaluating the performance of these approaches reducing all named entities to a single symbol “NE” to avoid topic-dependent features, the drop in accuracy is higher for the deep learning approach. Show more

Keywords: Convolutional neural networks, deep learning, author profiling, gender identification, language variety identification, machine learning, character n-grams, Spanish

DOI: 10.3233/JIFS-179032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4845-4855, 2019