32 research outputs found

    Positive unlab ele d learning for building recommender systems in a parliamentary setting

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    Our goal is to learn about the political interests and preferences of Members of Parliament (MPs) by mining their parliamentary activity in order to develop a recommendation/filtering system to determine how relevant documents should be distributed among MPs. We propose the use of positive unlabeled learning to tackle this problem since we only have information about relevant documents (the interventions of each MP in debates) but not about irrelevant documents and so it is not possible to use standard binary classifiers which have been trained with positive and negative examples. Additionally, we have also developed a new positive unlabeled learning algorithm that compares favorably with: (a) a baseline approach which assumes that every intervention by any other MP is irrelevant; (b) another well-known positive unlabeled learning method; and (c) an approach based on information retrieval methods that matches documents and legislators’ representations. The experiments have been conducted with data from the regional Spanish Andalusian Parliament.This work has been funded by the Spanish “Ministerio de Economía y Competitividad” under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

    OBOE: an Explainable Text Classification Framework

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    Explainable Artificial Intelligence (XAI) has recently gained visibility as one of the main topics of Artificial Intelligence research due to, among others, the need to provide a meaningful justification of the reasons behind the decision of black-box algorithms. Current approaches are based on model agnostic or ad-hoc solutions and, although there are frameworks that define workflows to generate meaningful explanations, a text classification framework that provides such explanations considering the different ingredients involved in the classification process (data, model, explanations, and users) is still missing. With the intention of covering this research gap, in this paper we present a text classification framework called OBOE (explanatiOns Based On concEpts), in which such ingredients play an active role to open the black-box. OBOE defines different components whose implementation can be customized and, thus, explanations are adapted to specific contexts. We also provide a tailored implementation to show the customization capability of OBOE. Additionally, we performed (a) a validation of the implemented framework to evaluate the performance using different corpora and (b) a user-based evaluation of the explanations provided by OBOE. The latter evaluation shows that the explanations generated in natural language express the reason for the classification results in a way that is comprehensible to non-technical users

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    the admissibility of AI- generated evidence

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    Durante as duas últimas décadas, a Inteligência Artificial tornou-se uma presença constante nas nossas vidas. Ao impactar setores relevantes da sociedade, tem relevando o seu caráter disruptivo, sendo um dos motores impulsionadores da Quarta Revolução Industrial. A Inteligência Artificial além dos seus presentes benefícios para a humanidade, promete soluções inovadoras para os problemas que afligem a sociedade contemporânea, porém a mesma comporta uma duplicidade de efeitos. Os sistemas de Inteligência Artificial pela sua capacidade de monitorizar o seu ambiente circundante, e autonomamente recolher, processar dados, aprender e agir, podem concretizar riscos para os direitos fundamentais, principalmente no contexto da justiça criminal. Esta análise irá focar-se nas especificidades dos sistemas dotados de Inteligência Artificial, aprofundando a temática da admissibilidade da prova gerada por Inteligência Artificial no quadro probatório do Direito Processual Penal Português à luz dos direitos de defesa do arguido e dos seus princípios que norteadores.During the last two decades Artificial Intelligence became ubiquitous in our lives. Revealing itself as a disruptive technology, it is already impacting important sectors of society, being a driver of the Fourth Industrial Revolution. Artificial Intelligence is benefiting humanity, and promises innovative solutions to modern-life problems, nevertheless it has a twofold effect. Artificial Intelligence as systems that are capable to monitor their surrounding environment, autonomously collect and process data, learn and act, may constitute harm to fundamental rights, mainly when deployed to criminal justice. This analysis will focus on the specificities of Artificial Intelligence systems, delving into the admissibility of AI-generated evidence in the Portuguese criminal evidentiary framework in light of the defence rights and structuring principles of Portuguese criminal procedure

    Semantic Systems. The Power of AI and Knowledge Graphs

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    This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

    Improving Search Effectiveness through Query Log and Entity Mining

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    The Web is the largest repository of knowledge in the world. Everyday people contribute to make it bigger by generating new web data. Data never sleeps. Every minute someone writes a new blog post, uploads a video or comments on an article. Usually people rely on Web Search Engines for satisfying their information needs: they formulate their needs as text queries and they expect a list of highly relevant documents answering their requests. Being able to manage this massive volume of data, ensuring high quality and performance, is a challenging topic that we tackle in this thesis. In this dissertation we focus on the Web of Data: a recent approach, originated from the Semantic Web community, consisting in a collective effort to augment the existing Web with semistructured-data. We propose to manage the data explosion shifting from a retrieval model based on documents to a model enriched with entities, where an entity can describe a person, a product, a location, a company, through semi-structured information. In our work, we combine the Web of Data with an important source of knowledge: query logs, which record the interactions between the Web Search Engine and the users. Query log mining aims at extracting valuable knowledge that can be exploited to enhance users’ search experience. According to this vision, this dissertation aims at improving Web Search Engines toward the mutual use of query logs and entities. The contributions of this work are the following: we show how historical usage data can be exploited for improving performance during the snippet generation process. Secondly, we propose a query recommender system that, by combining entities with queries, leads to significant improvements to the quality of the suggestions. Furthermore, we develop a new technique for estimating the relatedness between two entities, i.e., their semantic similarity. Finally, we show that entities may be useful for automatically building explanatory statements that aim at helping the user to better understand if, and why, the suggested item can be of her interest

    ArgMine: Argumentation Mining from Text

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    O objetivo da prospeção de argumentos a partir de texto é a deteção e identificação de forma automática da estrutura argumentativa contida num texto escrito em linguagem natural.Um argumento é uma estrutura retórica que é estudada desde à muitos anos e que se encontra bem fundamentada. De uma forma geral, argumentos são posições justificáveis onde factos (premissas) são apresentados em suporte de uma conclusão.A ambiguidade do texto escrito em linguagem natural, diferentes estilos de escrita, contexto implícito e a complexidade em construir estruturas argumentativas são alguns dos desafios que fazem desta tarefa muito desafiadora.Extraindo de forma automática argumentos a partir de texto, somos capazes de saber não apenas quais são os pontos de vista que estão a ser expressos, mas também quais são as razões para acreditar nesse pontos de vista. Assim sendo, a prospeção de argumentos de forma automática tem o potencial de trazer avanços em algumas áreas de investigação tais como prospeção de opiniões, sistemas de recomendação e sistemas multi-agente.A tarefa completa de prospeção de argumentos pode ser decomposta em várias sub-tarefas. Esta tese aborda a deteção e identificação, de forma automática, dos componentes argumentativos presentes no texto. Isso envolve detetar as zonas do texto que contêm conteúdo argumentativo e, a seleção dos fragmentos de texto que correspondem às unidades elementares de argumentos. Para que seja possível de uma forma automática detetar e identificar componentes argumentativos a partir de texto, algoritmos de aprendizagem máquina supervisionada serão usados.O conjunto de dados alvo que será usado para treinar os algoritmos são noticias escritas na língua Portuguesa.The aim of argumentation mining is the automatic detection and identification of the argumentative structure contained within a piece of natural language text. An argument is an ancient and well studied rhetorical structure. In a general form, arguments are justifiable positions where pieces of evidence (premises) are offered in support of a conclusion. The ambiguity of natural language text, different writing styles, implicit context and the complexity of building argument structures are some of the challenges which make this task very challenging. By automatically extracting arguments from text, we are able to tell not just what views are being expressed, but also what are the reasons to believe those particular views. Therefore, argumentation mining has the potential to improve some research topics such as opinion mining, recommender systems and multi-agent systems. The full task of argumentation mining can be decomposed into several subtasks. This thesis focuses on the automatic detection and identification of the argumentative components presented in the original text. This involves detecting the zones of text that contain argumentative content and the identification of fragments of text that will form the elementary units of the argument. In order to automatically detect and identify argumentative components in text, supervised machine learning algorithms will be used. The target corpus used to train the algorithms are news written in Portuguese language

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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