Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help

Monitorage de la mécanique respiratoire chez le patient ventilé : comment procéder au lit du malade [Monitoring respiratory mechanics in ventilated patients: a bedside approach]

In clinical practice, the term respiratory mechanics usually refers to the concept of compliance and resistance of the respiratory system. In ventilated patients, measurements of compliance and resistance can be performed at the bedside using the ventilator (end- inspiratory and end-expiratory occlusions). Those measurements allow caregivers to monitor pulmonary disorders and evaluate treatment effectiveness. In case of sudden change in compliance or resistance, the variation of flow and pressure curves displayed on the ventilator screen helps to narrow down the differential diagnosis. This article defines what are compliance and resistance and how to calculate and use them at the bedside

Modes ventilatoires de base en ventilation mécanique invasive [Classic ventilatory modes for invasive mechanical ventilation]

Invasive mechanical ventilation is part of the daily practice of the intensivist and anesthetist. The comprehensive knowledge of ventilatory modes is mandatory for managing the ventilated patients. The objective of this article is to explain the characteristics of the barometric and volumetric modes and the differences between controlled, assist-controlled, and assisted ventilation. The most common modes (volume and pressure assist-control, dual modes and pressure support) are described in detail. Parameters that must be set and those that must be monitored in each mode are also described. Finally, suggestions for initial settings are provided in order to offer the reader unfamiliar with mechanical ventilation a practical decision-making aid

Arguing from Similar Positions: An Empirical Analysis

Abstract. Argument-based deliberation dialogues are an important mechanism in the study of agent coordination, allowing agents to exchange formal arguments to reach an agreement for action. Agents participating in a deliberation dialogue may begin the dialogue with very similar sets of arguments to one another, or they may start the dialogue with disjoint sets of arguments, or some middle ground. In this paper, we empirically investigate whether the similarity of agents ’ arguments affects the dialogue outcome. Our results show that agents that have similar sets of initially known arguments are less likely to reach an agreement through dialogue than those that have dissimilar sets of initially known arguments.

Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles

International audience.The open nature of the Web exposes it to the many imper-fections of our world. As a result, before we can use knowledge obtainedfrom the Web, we need to represent that fuzzy, vague, ambiguous and un-certain information. Current standards of the Semantic Web and LinkedData do not support such a representation in a formal way and indepen-dently of any theory. We present a new vocabulary and a framework tocapture and handle uncertainty in the Semantic Web. First, we definea vocabulary for uncertainty and explain how it allows the publishingof uncertainty information relying on different theories. In addition, weintroduce an extension to represent and exchange calculations involvedin the evaluation of uncertainty. Then we show how this model and itsoperational definitions support querying a data source containing differ-ent levels of uncertainty metadata. Finally, we discuss the perspectiveswith a view on supporting reasoning over uncertain linke

Claim Detection in Judgments of the EU Court of Justice

Mining arguments from text has recently become a hot topic in Artificial Intelligence. The legal domain offers an ideal scenario to apply novel techniques coming from machine learning and natural language processing, addressing this challenging task. Following recent approaches to argumentation mining in juridical documents, this paper presents two distinct contributions. The first one is a novel annotated corpus for argumentation mining in the legal domain, together with a set of annotation guidelines. The second one is the empirical evaluation of a recent machine learning method for claim detection in judgments. The method, which is based on Tree Kernels, has been applied to context-independent claim detection in other genres such as Wikipedia articles and essays. Here we show that this method also provides a useful instrument in the legal domain, especially when used in combination with domain-specific information