Refining Implicit Argument Annotation for UCCA

Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.Comment: DMR 202

More than just friends? Facebook, disclosive ethics and the morality of technology

Social networking sites have become increasingly popular destinations for people wishing to chat, play games, make new friends or simply stay in touch. Furthermore, many organizations have been quick to grasp the potential they offer for marketing, recruitment and economic activities. Nevertheless, counterclaims depict such spaces as arenas where deception, social grooming and the posting of defamatory content flourish. Much research in this area has focused on the ends to which people deploy the technology, and the consequences arising, with a view to making policy recommendations and ethical interventions. In this paper, we argue that tracing where morality lies is more complex than these efforts suggest. Using the case of a popular social networking site, and concepts about the morality of technology, we disclose the ethics of Facebook as diffuse and multiple. In our conclusions we provide some reflections on the possibilities for action in light of this disclosure

A metaobject architecture for fault-tolerant distributed systems : the FRIENDS approach

The FRIENDS system developed at LAAS-CNRS is a metalevel architecture providing libraries of metaobjects for fault tolerance, secure communication, and group-based distributed applications. The use of metaobjects provides a nice separation of concerns between mechanisms and applications. Metaobjects can be used transparently by applications and can be composed according to the needs of a given application, a given architecture, and its underlying properties. In FRIENDS, metaobjects are used recursively to add new properties to applications. They are designed using an object oriented design method and implemented on top of basic system services. This paper describes the FRIENDS software-based architecture, the object-oriented development of metaobjects, the experiments that we have done, and summarizes the advantages and drawbacks of a metaobject approach for building fault-tolerant system

Co-Following on Twitter

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when (i) the most discriminative features are removed and (ii) only relatively "sparse" users with fewer than 152 but more than 43 Twitter friends are considered. Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between Apple and Puma and, less obviously, between Nike and Coca-Cola. Concerning a user's popularity we find a statistically significant connection between having a more "average" followership and having more followers than direct rivals. Interestingly, a \emph{larger} audience also seems to be linked to a \emph{less diverse} audience in terms of their co-following.Comment: full version of a short paper at Hypertext 201

Quakers and Creation Care: Potentials and Pitfalls for an Ecotheology of Friends (Chapter Five in Quakers, Creation Care, and Sustainability)

While Friends have a strong tradition of activism around the social justice issues of each era, we also tend to spiritualize our faith, disconnecting it from the material world. Environmental concerns are arguably one of the most important social justice issues of of our time, and in many ways, activism, advocacy, and lifestyle witness seem like natural ways for Friends to engage in social justice in this time in history. This essay will explore some of the historical and theological strengths Friends can draw from our tradition that can help build a particularly Quaker ecotheology, as well as some of the portions of the Friends tradition that get in the way of practicing our faith in a more sustainable way

Robustness analysis of graph-based machine learning

Graph-based machine learning is an emerging approach to analysing data that is or can be well-modelled by pairwise relationships between entities. This includes examples such as social networks, road networks, protein-protein interaction net- works and molecules. Despite the plethora of research dedicated to designing novel machine learning models, less attention has been paid to the theoretical proper- ties of our existing tools. In this thesis, we focus on the robustness properties of graph-based machine learning models, in particular spectral graph filters and graph neural networks. Robustness is an essential property for dealing with noisy data, protecting a system against security vulnerabilities and, in some cases, necessary for transferability, amongst other things. We focus specifically on the challenging and combinatorial problem of robustness with respect to the topology of the underlying graph. The first part of this thesis proposes stability bounds to help understand to which topological changes graph-based models are robust. Beyond theoretical results, we conduct experiments to verify the intuition this theory provides. In the second part, we propose a flexible and query-efficient method to perform black-box adversarial attacks on graph classifiers. Adversarial attacks can be considered a search for model instability and provide an upper bound between an input and the decision boundary. In the third and final part of the thesis, we propose a novel robustness certificate for graph classifiers. Using a technique that can certify in- dividual parts of the graph at varying levels of perturbation, we provide a refined understanding of the perturbations to which a given model is robust. We believe the findings in this thesis provide novel insight and motivate further research into both understanding stability and instability of graph-based machine learning models

Spartan Daily, May 14, 2002

Volume 118, Issue 71https://scholarworks.sjsu.edu/spartandaily/10643/thumbnail.jp

Using dialogue corpora to extend information extraction patterns for natural language understanding of dialogue

This work was funded by the Companions project (www.companions-project.org) sponsored by the European Commission as part of the Information Society Technologies (IST) programme under EC grant number IST-FP6-034434.This paper examines how Natural Language Process (NLP) resources and online dialogue corpora can be used to extend coverage of Information Extraction (IE) templates in a Spoken Dialogue system. IE templates are used as part of a Natural Language Understanding module for identifying meaning in a user utterance. The use of NLP tools in Dialogue systems is a difficult task given spoken dialogue is often not well-formed and 2) there is a serious lack of dialogue data. In spite of that, we have devised a method for extending IE patterns using standard NLP tools and available dialogue corpora found on the web. In this paper, we explain our method which includes using a set of NLP modules developed using GATE (a General Architecture for Text Engineering), as well as a general purpose editing tool that we built to facilitate the IE rule creation process. Lastly, we present directions for future work in this area.peer-reviewe