Improving Sentence-level Subjectivity Classification through Readability Measurement

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 168-147. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Dynamics of the Amount of Control in Offshore Software Development Projects

This paper investigates changes in the variety and intensity of formal and informal control mechanisms in offshore software development (OSD) projects. Based on a comparative case study approach our results confirm existing findings such as that the amount of control varies across different projects stages, but also contribute with new findings. For example, we found that particularly the quality of project deliverables in early project phases will lead to an increase of the amount of formal control. However, these quality problems do not necessarily lead to an increase of informal control. In return, an increase in quality of deliverables will subsequently decrease the amount of control. An important finding is that in contrast to prior studies our results do not support that the amount of control is directly related to project success. Altogether, our study contributes to the further understanding of the dynamics of the amount of control, its influencing factors and its relationship to project success

Genre and Domain Dependencies in Sentiment Analysis

Genre and domain influence an author\''s style of writing and therefore a text\''s characteristics. Natural language processing is prone to such variations in textual characteristics: it is said to be genre and domain dependent. This thesis investigates genre and domain dependencies in sentiment analysis. Its goal is to support the development of robust sentiment analysis approaches that work well and in a predictable manner under different conditions, i.e. for different genres and domains. Initially, we show that a prototypical approach to sentiment analysis -- viz. a supervised machine learning model based on word n-gram features -- performs differently on gold standards that originate from differing genres and domains, but performs similarly on gold standards that originate from resembling genres and domains. We show that these gold standards differ in certain textual characteristics, viz. their domain complexity. We find a strong linear relation between our approach\''s accuracy on a particular gold standard and its domain complexity, which we then use to estimate our approach\''s accuracy. Subsequently, we use certain textual characteristics -- viz. domain complexity, domain similarity, and readability -- in a variety of applications. Domain complexity and domain similarity measures are used to determine parameter settings in two tasks. Domain complexity guides us in model selection for in-domain polarity classification, viz. in decisions regarding word n-gram model order and word n-gram feature selection. Domain complexity and domain similarity guide us in domain adaptation. We propose a novel domain adaptation scheme and apply it to cross-domain polarity classification in semi- and unsupervised domain adaptation scenarios. Readability is used for feature engineering. We propose to adopt readability gradings, readability indicators as well as word and syntax distributions as features for subjectivity classification. Moreover, we generalize a framework for modeling and representing negation in machine learning-based sentiment analysis. This framework is applied to in-domain and cross-domain polarity classification. We investigate the relation between implicit and explicit negation modeling, the influence of negation scope detection methods, and the efficiency of the framework in different domains. Finally, we carry out a case study in which we transfer the core methods of our thesis -- viz. domain complexity-based accuracy estimation, domain complexity-based model selection, and negation modeling -- to a gold standard that originates from a genre and domain hitherto not used in this thesis

Control Perception Differences in IS Offshoring Projects: Conceptualization and Empirical Test of Performance Impact

This paper takes a novel approach to IS project control by studying control perceptions of clients and vendors in IS offshoring projects and the implications of their perceptions for project performance. We present the results of a survey-based analysis of 46 client-vendor dyads involved in IS offshoring projects. A major contribution of this study lies in operationalizing and empirically testing attempted control (control perceived by the client) and realized control (control perceived by the vendor). Based on prior research, we employ a relational governance view to test whether control perception differences decrease IS project performance. Building on transaction cost economics, we then develop and test the rival perspective that control perception differences may improve performance. Our data support the view that perception differences can be beneficial for IS offshoring project performance

New constraints on Saturn's interior from Cassini astrometric data

This work has been supported by the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement 263466 for the FP7-ESPaCE project, the International Space Science Institute (ISSI), PNP (INSU/CNES) and AS GRAM (INSU/CNES/INP). The work of R. A. J. was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. N.C. and C.M. were supported by the UK Science and Technology Facilities Council (Grant No. ST/M001202/1) and are grateful to them for financial assistance. C.M. is also grateful to the Leverhulme Trust for the award of a Research Fellowship. N.C. thanks the Scientific Council of the Paris Observatory for funding. S. Mathis acknowledge funding by the European Research Council through ERC grant SPIRE 647383

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques

Abstract This paper describes University of Leipzig's approach to SemEval-2013 task 2B on Sentiment Analysis in Twitter: message polarity classification. Our system is designed to function as a baseline, to see what we can accomplish with well-understood and purely data-driven lexical features, simple generalizations as well as standard machine learning techniques: We use one-against-one Support Vector Machines with asymmetric cost factors and linear "kernels" as classifiers, word uni-and bigrams as features and additionally model negation of word uni-and bigrams in word n-gram feature space. We consider generalizations of URLs, user names, hash tags, repeated characters and expressions of laughter. Our method ranks 23 out of all 48 participating systems, achieving an averaged (positive, negative) F-Score of 0.5456 and an averaged (positive, negative, neutral) F-Score of 0.595, which is above median and average