Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

/pdf/social-media-and-fake-news-in-the-2016-election-1m4ttkb5mo.pdf

Social Media and Fake News in the 2016 Election

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

/pdf/multi-label-classification-an-overview-3lubavw9dw.pdf

Multi-label classification: An overview

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., I don’t know) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as the objective function in neural models. Experimental results demonstrate that the proposed MMI models produce more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets and in human evaluations.

/pdf/a-diversity-promoting-objective-function-for-neural-jymgikudii.pdf

A Diversity-Promoting Objective Function for Neural Conversation Models

Research on question answering tools over open linked data is increasing, and that brings the necessity of resources to allow for the evaluation and comparison of such systems. Question Answering over Linked Data (QALD) is a traditional benchmark event that occurs annually since 2011. However, although the multilingual task is available since its third edition, there is a necessity to foster the actual Portuguese Language resources present in this event benchmark. In this paper, we describe the development of the Portuguese language as a QALD corpus complement. The corpus is based on an existing QALD multilingual corpus and comprises 258 sentences used for the event challenge in 2017. We constructed a second corpus to allow direct comparison with the DBPedia Portuguese content. The main topics to highlight are the adopted methodology, which results in corpus related to frequent Brazilian Portuguese use of the language, and the work on adapting the answers to the DBPedia PT knowledge base, providing a corpus to evaluate Portuguese QA systems accurately.

A Portuguese Dataset for Evaluation of Semantic Question Answering

Technology is the tool that is being used in the various sectors of life and medical is one of them. Electronic medical records (EMR) are now widely used instead of physical documents. This paper aims to achieve continuing challenges of MedNLP task series in NTCIR-10 and 11. In these tasks, it already attempted named entity recognition (NER) and evaluated the term normalization technology from medical reports written in Japanese, whereas, this task are more advantage, practical and closer to reality application for the medical industry. This task divided into 2 subtasks: (Task1) Phenotyping task requires giving a standard disease names from given medical records, (Task2) creative task to make up ideas to utilize resulting products in the real world. This paper focuses on using tag of speech and improve NER to correctly get sequences of words string in order to achieve the ICD. The experimental result has not shown quite high performance (precision major: 9.6%, recall major: 4.4%, F-measure major: 6.0%). However, it strongly shows a promising result from an international non-speaking Japanese group.

/pdf/ue-ud-at-ntcir-12-mednlpdoc-task-1xnwh4eibq.pdf

UE-UD at NTCIR-12 MedNLPDoc Task.

In this paper the process of coreference annotation in Portuguese texts in the context of a task of IberEval 2017 is described and the main observed problems are discussed. The work was done by a team of researchers from the Centre for Linguistics of the University of Lisbon (CLUL) and from the Computer Science Department of the University of Évora (UE). Due to time constraints and the complexity of the task, only researchers from CLUL were able to finish successfully the annotation process. The main problems are presented and discussed and some possible solutions are proposed. Nevertheless, the obtained results are similar with the overall results of the task.

http://ceur-ws.org/Vol-1881/Portuguese_paper_1.pdf

The Annotation Coreference Task at IberEval'2017: The Experience of CLUL/UE.

Automatic reasoning about textual information is a challenging task in modern Natural Language Processing (NLP) systems. In this work we describe our proposal for representing and reasoning about Portuguese documents by means of Linked Data like ontologies and thesauri. Our approach resorts to a specialized pipeline of natural language processing (part-of-speech tagger, named entity recognition, semantic role labeling) to populate an ontology for the domain of criminal investigations. The provided architecture and ontology are language independent. Although some of the NLP modules are language dependent, they can be built using adequate AI methodologies.

From Textual Information Sources to Linked Data in the Agatha Project

Dynamic logic programming allows the representation and the inference of evolving knowledge. Legal knowledge reasoning needs the capability to model laws that change over time and to model laws produced by distinct entities with different priorities at different time points. In this paper we propose the use of dynamic logic programming to model these legal dynamic situations. Some examples are discussed and the implementation of a legal oracle server is described.

/pdf/using-dynamic-logic-programming-to-model-legal-reasoning-17kuv3r24z.pdf

Using dynamic logic programming to model legal reasoning.

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