Journal of Intelligent & Fuzzy Systems - Volume 39, issue 2

Authors: Shweta, | Sanyal, Ratna

Article Type: Research Article

Abstract: In this research work, we propose a rule based approach for the automatic extraction of UML diagram from the unstructured format of software functional requirements. The existing work provides decent results for active sentences and positive sentences but the challenge in our work is to automatic extract class diagram elements from passive voice type sentences and negative sentences. Furthermore, there is scope to do more research in extraction process using multi-word terms. Thus, we have endeavored to automatic extract the class diagram elements by overcoming these challenges. The methodology uses the Stanford CoreNLP Tools along with Java for the practical …implementation of formulated rules. Our approach has proved that without supplant the human being and their decision making, one could reduce the human effort while designing functional requirements. Several case studies were performed to compare class diagrams generated by our methodology to the ones created by experts. Our methodology outperforms the existing work and provides impressive Average completeness (0.82), Average correctness (0.92) and Average redundancy (0.15). Results show that class diagram elements extracted by our methodology are precise as well as accurate and hence, in practice, such class diagrams would be a good preliminary diagram to converge towards to precise and comprehensive class diagrams. Show more

Keywords: Unified modeling language, class diagram, natural language processing, functional requirements

DOI: 10.3233/JIFS-179871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2047-2059, 2020

Authors: Pinto, David | Priego, Belém

Article Type: Research Article

Abstract: Automatic validation of compositionality vs non-compositionality is a very challenging problem in NLP. A very small number of papers in literature report results in this particular problem. Recently, some new approaches have arised with respect to this particular linguistic task. One of these approaches that have called our attention is based on what authors call “lexical domain”. In this paper, we analyze the use of Pointwise Mutual Information for constructing thesauri on the fly, which can be further employed instead of dictionaries for determining whether or not a given phraseological unit is compositional or not. The experimental results carried out …in this paper show that this dissimilarity measure (PMI), can effectively be used when determining compositionality of a given verbal phraseological unit. Moreover, we show that the use of thesauri improves the results obtained in comparison with those experiments employing dictionaries, highlighting the use of self-constructed lexical resources which are, in fact, taking advantage of the same vocabulary of the target dataset. Show more

Keywords: Multiword expression, compositionality, pointwise mutual information, thesaurus

DOI: 10.3233/JIFS-179872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2061-2070, 2020

Authors: Bhatnagar, Sahil | Chatterjee, Niladri

Article Type: Research Article

Abstract: Translation has been one of the oldest problems in natural language processing. Despite its age, it is still one where there is a tremendous scope for improvement and creativity; the quantity and quality of research in it is testament to that fact. The subfield of primarily using deep neural networks for translation has recently started to gain traction. Many techniques have been developed using deep encoder-decoder networks for bilingual translation using both parallel as well as non-parallel corpora. There is a lot of potential in applying concepts such as bilingual embeddings to create generic translation architecture, which doesn’t need huge …parallel corpora to train. These ideas are particularly pertinent in the case of Indic languages, where it is generally difficult to obtain such corpus. In this paper, we try to adapt some of newest techniques in autoencoder networks and bilingual embeddings to the task of translating between English and Hindi. The models considerably outperform state of the art translating systems for these languages. Show more

Keywords: Bilingual embeddings, machine translation, autoencoder

DOI: 10.3233/JIFS-179873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2071-2079, 2020

Authors: García-Gorrostieta, Jesús Miguel | López-López, Aurelio | González-López, Samuel

Article Type: Research Article

Abstract: The argumentation in academic writings is necessary to clearly communicate the ideas of the students. The relations between argumentative components are an essential part since this shows the contrast or support of the presented ideas. In this paper, we present two approaches to relation identification between pairs of components. In the first, we detect initially which components are related, to later classify them in support or attack relation. In the second approach, we identify directly which components have a support relation. For these approaches, we employed machine learning techniques with representations of several lexical, syntactic, semantic, structural and indicator features. …Experiments in argumentative sections of academic theses showed that the models achieve encouraging results solving the task, and revealing the argumentative structures prevailing in student writings. Show more

Keywords: Argument component relation, argument mining, academic writing, argumentation studies, annotated theses corpus

DOI: 10.3233/JIFS-179874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2081-2091, 2020

Authors: Vázquez, Eder Vázquez | Ledeneva, Yulia | García-Hernández, René Arnulfo

Article Type: Research Article

Abstract: Despite advances in medical safety, errors related to adverse drug reactions are still very common. The most common reason for a patient to develop an adverse reaction to a medication is confusion over the prescribed medication. The similarity of drug names (by their spelling or phonetic similarity) is recognized as the most critical factor causing medication confusion. Several studies have studied techniques for the identification of confusing medications pairs, the most important of which employ techniques based on similarity measures that indicate the degree of similarity that exists between two drugs names. Although it generates good results in the identification …of confusing drug names, each of the similarity measures used detects to a greater or lesser degree of similarity that exists between a pair. Recent studies indicate that the optimized combination of several similarity measures can generate better results than the individual application of each one. This paper presents an optimized method of combining various similarity measures based on symbolic regression. The obtained results show an improvement in the identification of confusing drug names. Show more

Keywords: Confusing drug names, symbolic regression, look-alike, sound-alike, similarity measures

DOI: 10.3233/JIFS-179875

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2093-2103, 2020

Authors: Vázquez, Andrés | Pinto, David | Pallares, Juan | De la Rosa, Rafael | Tecotl, Elia

Article Type: Research Article

Abstract: In this work, we present a model for the automatic generation of written dialogues, through the use of grammatical inference. This model allows the automatic recognition of grammars from a set dialogues employed as a training set. The inferred grammars are then used to generate templates of responses within the dialogues. The final objective is to apply this model in a specific domain dialogue system that answers questions in Spanish with the use of a knowledge base. The experiments carried out have been performend using the DIHANA project corpus which contains dialogues written in Spanish about schedules and prices of …a rail system. Show more

Keywords: Grammatical inference, dialogue system, knowledge base

DOI: 10.3233/JIFS-179876

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2105-2113, 2020

Authors: Garcés-Báez, Alfonso | López-López, Aurelio

Article Type: Research Article

Abstract:  When people communicate, we often face situations where decisions have to be made, regardless of silence of one of the interlocutors. That is, we have to decide from incomplete information, guessing the intentions of the silent person. Implicatures allow to make inferences from what is said, but we can also infer from omission, or specifically from intentional silence in a conversation. In some contexts, not saying p generates a conversational implicature: that the speaker did not have sufficient reason, all things considered, to say p . This behaviour has been studied by several disciplines but barely touched in logic …or artificial intelligence. After reviewing some previous studies of intentional silence and implicature, we formulate a semantics with five different interpretations of omissive implicature, in terms of the Says() predicate, and focus on puzzles involving assertions or testimonies, to analyze their implications. Several conclusions are derived from the different possibilities that were opened for analysis after taking into account silence. Finally, we develop a general strategy for the use of the proposed semantics in cases involving some kind of silence. Show more

Keywords: Intentional silence, omission, omissive implicature, logic, semantics, says predicate, answer set programming

DOI: 10.3233/JIFS-179877

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2115-2126, 2020

Authors: Beltrán, Beatriz | Vilariño, Darnes | Martínez-Trinidad, José Fco. | Carrasco-Ochoa, J.A. | Pinto, David

Article Type: Research Article

Abstract: Overlapping clustering algorithms have shown to be effective for clustering documents. However, the current overlapping document clustering algorithms produce a big number of clusters, which make them little useful for the user. Therefore, in this paper, we propose a k-means based method for overlapping document clustering, which allows to specify by the user the number of groups to be built. Our experiments with different corpora show that our proposal allows obtaining better results in terms of FBcubed than other recent works for overlapping document clustering reported in the literature.

Keywords: Clustering, overlapping clustering, document clustering

DOI: 10.3233/JIFS-179878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2127-2135, 2020

Authors: Bejos, Sebastián | Feliciano-Avelino, Ivan | Martínez-Trinidad, J. Fco. | Carrasco-Ochoa, J. A.

Article Type: Research Article

Abstract: Document clustering has become an important task for processing the big amount of textual information available on the Internet. On the other hand, k-means is the most widely used algorithm for clustering, mainly due to its simplicity and effectiveness. However, k-means becomes slow for large and high dimensional datasets, such as document collections. Recently the FPAC algorithm was proposed to mitigate this problem, but the improvement in the speed was reached at the cost of reducing the quality of the clustering results. For this reason, in this paper, we introduce an improved FPAC algorithm, which, according our experiments on different …document collections, allows obtaining better clustering results than FPAC, without highly increasing the runtime. Show more

Keywords: Document clustering, large collection, high dimensionality

DOI: 10.3233/JIFS-179879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2137-2145, 2020

Authors: Hernández Farías, Delia Irazú | Prati, Ronaldo | Herrera, Francisco | Rosso, Paolo

Article Type: Research Article

Abstract: Irony detection is a not trivial problem and can help to improve natural language processing tasks as sentiment analysis. When dealing with social media data in real scenarios, an important issue to address is data skew, i.e. the imbalance between available ironic and non-ironic samples available. In this work, the main objective is to address irony detection in Twitter considering various degrees of imbalanced distribution between classes. We rely on the emotIDM irony detection model. We evaluated it against both benchmark corpora and skewed Twitter datasets collected to simulate a realistic distribution of ironic tweets. We carry out a set …of classification experiments aimed to determine the impact of class imbalance on detecting irony, and we evaluate the performance of irony detection when different scenarios are considered. We experiment with a set of classifiers applying class imbalance techniques to compensate class distribution. Our results indicate that by using such techniques, it is possible to improve the performance of irony detection in imbalanced class scenarios. Show more

Keywords: Irony detection, class imbalance, imbalanced learning

DOI: 10.3233/JIFS-179880

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2147-2163, 2020