Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Work, version, text and scriptum: high medieval manuscript terminology in the aftermath of the new philology

This article reviews the terminological framework to describe manuscripts. The Lachmannian terminology allows scholars to classify manuscripts as versions or variants of a work on a purely textual basis, but lacks a rigid designator to indicate a (part of a) manuscript as a unit of text and material considerations. Conversely, scholars who adopt Dagenais’ solution to renounce the work and concentrate on the material scriptum gain a rigid designator, but threaten to lose the ability to classify manuscripts at all. Proceeding from a case study, the article argues that the twelfth-century view of a work’s ontological status enables medievalists to keep classifying their scripta on both textual and material grounds. It explores the possibility of using Dagenais’ scriptum as the foundation for a Neo-Lachmannian terminological framework that allows scholars to study manuscript variance and materiality without losing the ability to classify them

The Relations Between Pedagogical and Scientific Explanations of Algorithms: Case Studies from the French Administration

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based on field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures

Defining Syria: An Analysis of Terminological Selection of the Syrian Crisis (2010-2014) by Sky News and BBC News and its Implications for Audiences

This thesis is seeking to further the understanding of how news broadcasters in Britain choose and use terminology and how this affects audiences. BBC News and Sky News have been selected as the case studies on which to represent the UK news broadcasting industry. These two broadcasters view themselves as different and so do the public and the literature and this research is aiming to show how different or similar these two broadcasters are through independent surveys, interviews and lexical analysis, culminating in a conclusion as to the extent of the encoder/decoder relationships around key terms of the Syrian Crisis news coverag

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Human Associations Help to Detect Conventionalized Multiword Expressions

In this paper we show that if we want to obtain human evidence about conventionalization of some phrases, we should ask native speakers about associations they have to a given phrase and its component words. We have shown that if component words of a phrase have each other as frequent associations, then this phrase can be considered as conventionalized. Another type of conventionalized phrases can be revealed using two factors: low entropy of phrase associations and low intersection of component word and phrase associations. The association experiments were performed for the Russian language

Can an ethical revival of prudence within prudential regulation tackle corporate psychopathy?

The view that corporate psychopathy played a significant role in causing the global financial crisis, although insightful, paints a reductionist picture of what we present as the broader issue. Our broader issue is the tendency for psychopathy, narcissism and Machiavellianism to cluster psychologically and culturally as ‘dark leadership’ within global financial institutions. Strong evidence for their co-intensification across society and in corporations ought to alarm financial regulators. We argue that an ‘ethical revival’ of prudence within prudential regulation ought to be included in any package of solutions. Referencing research on moral muteness and the role of language in framing thoughts and behaviours, we recommend that regulators define prudence in an explicitly normative sense, an approach that may be further strengthened by drawing upon a widely appealing ethic of intergenerational care. An ethical revival of prudence, we argue, would allow the core problems of greed and myopia highlighted by corporate psychopathy theory to be addressed in a politically sensitive manner which recognises the pitfalls of regulating directly against corporate psychopathy. Furthermore, it would provide a viable conceptual framework to guide regulators along the treacherous path to more intrusive cultural regulation

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature