UoS: a graph-based system for graded word sense induction

This paper presents UoS, a graph-based Word Sense Induction system which attempts to find all applicable senses of a target word given its context, grading each sense according to its suitability to the context. Senses of a target word are induced through use of a non-parameterised, linear-time clustering algorithm that returns maximal quasi-strongly connected components of a target word graph in which vertex pairs are assigned to the same cluster if either vertex has the highest edge weight to the other. UoS participated in SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded Senses. Two system were submitted; both systems returned results comparable with those of the best performing systems

Cultural Diversity Professional Development for Teachers: A Research Brief

Many regions of the United States have experienced rapid shifts in the racial and ethnic makeup of the population over the past decade. As a result, many of the nation’s schools have undergone significant changes in student demographics. This includes growth in the numbers of Asian, Hispanic, and multi-racial students.1 Regionally, enrollment data reflect this demographic change (figure 1). It is also worth noting that these demographic changes are more dramatic in some schools and communities than others. Aggregate data for the region shows the largest growth in the population of Hispanic students, which increased from 2.98% of the population in the 2003- 04 school year to 10.23% in the 2015-16 school year. This demographic shift has increased the cultural and language diversity of our schools

Philanthropy in the News

Over the last two decades, the quantity of news coverage of foundations has gradually risen, but its quality remains highly superficial, according to this report by Philanthropy Awareness Initiative and University of Minnesota professor David Fan. In fact, nearly 99% of more than 40,000 stories since 1990 have been transactional in their content -- focused on grants made and dollars out the door, not on benefits achieved

What the loud “No” in the Greek referendum means for the Eurozone

In the late hours of Sunday evening, it was confirmed that the ‘No’ camp had won an unexpectedly resounding victory in the Greek referendum—by 61% to 39% the Greek people had chosen to reject the terms of the most recent bailout proposal from their creditors (the ECB, the EU and the IMF). When Alexis Tsipras, the Greek prime minister and leader of the radical left party Syriza, addressed his people following the result, he claimed it as a “victory of democracy”. Here, David Hope discusses the referendum, and why democracy may actually make it harder to strike a deal to keep Greece in the Eurozone

Evaluation of Modification of the Upper Batavia Dam on the Fox River, Illinois

Progress Report, Federal Aid Project F-136-R Segment 6Report issued on: August 2004Submitted to Office of Water Resources, Illinois Department of Natural Resource

Growth models, varieties of capitalism and macroeconomics

Lucio Baccaro and Jonas Pontusson make a significant contribution to comparative political economy with their approach to analyzing growth in advanced economies, which focuses on the demand side of the economy and distributive conflict. In contrast to Baccaro and Pontusson, however, we view their approach as reinforcing recent developments in varieties of capitalism rather than undermining them. We also believe that the type of modern macroeconomics used by (for instance) Carlin and Soskice is better placed than their post-Keynesian framework to analyze growth models, and that their approach is not inconsistent with it. Modern macroeconomic models incorporate, as they do not, a role for the state—including monetary and fiscal policy—and provide a coherent framework within which to analyze both the supply and demand sides of the economy; they also enable us to understand the interactions between economies and hence the role of growth models in global imbalances

Graph-based approaches to word sense induction

This thesis is a study of Word Sense Induction (WSI), the Natural Language Processing (NLP) task of automatically discovering word meanings from text. WSI is an open problem in NLP whose solution would be of considerable benefit to many other NLP tasks. It has, however, has been studied by relatively few NLP researchers and often in set ways. Scope therefore exists to apply novel methods to the problem, methods that may improve upon those previously applied. This thesis applies a graph-theoretic approach to WSI. In this approach, word senses are identifed by finding particular types of subgraphs in word co-occurrence graphs. A number of original methods for constructing, analysing, and partitioning graphs are introduced, with these methods then incorporated into graphbased WSI systems. These systems are then shown, in a variety of evaluation scenarios, to return results that are comparable to those of the current best performing WSI systems. The main contributions of the thesis are a novel parameter-free soft clustering algorithm that runs in time linear in the number of edges in the input graph, and novel generalisations of the clustering coeficient (a measure of vertex cohesion in graphs) to the weighted case. Further contributions of the thesis include: a review of graph-based WSI systems that have been proposed in the literature; analysis of the methodologies applied in these systems; analysis of the metrics used to evaluate WSI systems, and empirical evidence to verify the usefulness of each novel method introduced in the thesis for inducing word senses