Natural language processing and information systems : 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, Paris, France, June 13-15, 2018, Proceedings

This book constitutes the refereed proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, held in Paris, France, in June 2018. The 18 full papers, 26 short papers, and 9 poster papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based Models and Applications; Neural Networks Based Approaches; Ontology Engineering; NLP; Text Similarities and Plagiarism Detection; Text Classification; Information Mining; Recommendation Systems; Translation and Foreign Language Querying; Software Requirement and Checking

Natural Language Processing and Information Systems. 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, Paris, France, June 13-15, 2018, Proceedings

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Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

Automatic symptom identification plays a crucial role in assisting doctors during the diagnosis process in Telemedicine. In general, physicians spend considerable time on clinical documentation and symptom identification, which is unfeasible due to their full schedule. With text-based consultation services in telemedicine, the identification of symptoms from a user’s consultation is a sophisticated process and time-consuming. Moreover, at Altibbi, which is an Arabic telemedicine platform and the context of this work, users consult doctors and describe their conditions in different Arabic dialects which makes the problem more complex and challenging. Therefore, in this work, an advanced deep learning approach is developed consultations with multi-dialects. The approach is formulated as a multi-label multi-class classification using features extracted based on AraBERT and fine-tuned on the bidirectional long short-term memory (BiLSTM) network. The Fine-tuning of BiLSTM relies on features engineered based on different variants of the bidirectional encoder representations from transformers (BERT). Evaluating the models based on precision, recall, and a customized hit rate showed a successful identification of symptoms from Arabic texts with promising accuracy. Hence, this paves the way toward deploying an automated symptom identification model in production at Altibbi which can help general practitioners in telemedicine in providing more efficient and accurate consultations

THE OTHERLESS OTHER: LAS PASIONES MISÓGINAS DE LA MANOSFERA ESPAÑOLA

El objetivo de este artículo es identificar las actitudes y las pasiones que emergen en los discursos misóginos de los dos foros más populares de la Manosfera española: Forocoches y Burbuja.info. La hipótesis subyacente al análisis semiótico realizado postula la correlación entre, por un lado, las pasiones de los cuatro microsistemas patémicos que configuran el dispositivo actancial de los celos (Greimas y Fontanille, 1991) y, por otro lado, los discursos de odio contra las mujeres en las subculturas más relevantes de la Manosfera: MRA (Men Rights Activists), MGTOW (Men Going Their Own Way), PUA (Pick up Artists) e Incels (Involuntary Celibates). Los mensajes de ambos foros figurativizan la misoginia a través de la expresión de dos “estados del alma”, la indignación y el desprecio, cuya configuración axiológica nos permite determinar los rasgos distintivos y las interacciones que caracterizan el simulacro pasional de los sujetos misóginos

The otherless other : las pasiones misógenas de la manosfera española

El objetivo de este artículo es identificar las actitudes y las pasiones que emergen en los discursos misóginos de los dos foros más populares de la Manosfera española: Forocoches y Burbuja.info. La hipótesis subyacente al análisis semiótico realizado postula la correlación entre, por un lado, las pasiones de los cuatro microsistemas patémicos que configuran el dispositivo actancial de los celos (Greimas y Fontanille, 1991) y, por otro lado, los discursos de odio contra las mujeres en las subculturas más relevantes de la Manosfera: MRA (Men Rights Activists), MGTOW (Men Going Their Own Way), PUA (Pick up Artists) e Incels (Involuntary Celibates). Los mensajes de ambos foros figurativizan la misoginia a través de la expresión de dos "estados del alma", la indignación y el desprecio, cuya configuración axiológica nos permite determinar los rasgos distintivos y las interacciones que caracterizan el simulacro pasional de los sujetos misóginos.This article aims to identify the attitudes and passions in the misogynist discourses of the two most popular forums of the Spanish Manosphere: Forocoches and Burbuja.info. The hypothesis underlying the semiotic analysis carried out assumes the relationship between, on the one hand, the passions of the four pathemic microsystems that configure the actantial system of jealousy (Greimas and Fontanille, 1991) and, on the other hand, the hate speeches against women in the most relevant subcultures of the Manosphere: MRA (Men Rights Activists), MGTOW (Men Going Their Own Way), PUA (Pick up Artists) and Incels (Involuntary Celibates). The messages of both forums figurativize misogyny through the discursive construction of two "states of the soul": indignation and contempt, whose axiological configuration allows us to determine the characteristics and interactions of the passional simulacrum articulated by the misogynist subjects

Mining app reviews to support software engineering

The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models