Typicality, graded membership, and vagueness

This paper addresses theoretical problems arising from the vagueness of language terms, and intuitions of the vagueness of the concepts to which they refer. It is argued that the central intuitions of prototype theory are sufficient to account for both typicality phenomena and psychological intuitions about degrees of membership in vaguely defined classes. The first section explains the importance of the relation between degrees of membership and typicality (or goodness of example) in conceptual categorization. The second and third section address arguments advanced by Osherson and Smith (1997), and Kamp and Partee (1995), that the two notions of degree of membership and typicality must relate to fundamentally different aspects of conceptual representations. A version of prototype theory—the Threshold Model—is proposed to counter these arguments and three possible solutions to the problems of logical selfcontradiction and tautology for vague categorizations are outlined. In the final section graded membership is related to the social construction of conceptual boundaries maintained through language use

Automatic Extraction of Subcategorization from Corpora

We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora. Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English. An initial experiment, on a sample of 14 verbs which exhibit multiple complementation patterns, demonstrates that the technique achieves accuracy comparable to previous approaches, which are all limited to a highly restricted set of subcategorization classes. We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount.Comment: 8 pages; requires aclap.sty. To appear in ANLP-9

Robust Loss Functions under Label Noise for Deep Neural Networks

In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and on algorithms for estimating true labels from noisy labels. An alternate approach would be to look for loss functions that are inherently noise-tolerant. For binary classification there exist theoretical results on loss functions that are robust to label noise. In this paper, we provide some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems. These results generalize the existing results on noise-tolerant loss functions for binary classification. We study some of the widely used loss functions in deep networks and show that the loss function based on mean absolute value of error is inherently robust to label noise. Thus standard back propagation is enough to learn the true classifier even under label noise. Through experiments, we illustrate the robustness of risk minimization with such loss functions for learning neural networks.Comment: Appeared in AAAI 201

Environmental valuation, ecosystem services and aquatic species

The thesis consists of an introduction and four articles that can be read independently of each other. The common topic is environmental valuation and cost-benefit analysis. The applications relates to the growing concern of invasive species, and to waterpower externalities. In broad terms, all of the articles relates to water management. Article 1: "A Cost-Benefit analysis of introducing a non-native species: the case of signal crayfish in Sweden", assesses the economic impact of introducing the signal crayfish into a Swedish lake. Two scenarios are set up and compared. The first one assumes that there is no introduction of signal crayfish, so that the noble crayfish is preserved. In the second scenario, the signal crayfish is introduced, which immediately wipes out the entire stock of noble crayfish. The values of noble- and signal crayfish populations are measured as present values of their net future revenues. The values are than compared and net benefit of an introduction is calculated. The result indicate that net benefit of an introduction is positive if the intrinsic growth rate or the carrying capacity of the noble crayfish is below 40 % that of the signal crayfish. Article 2: "Assessing management options for weed control with demanders and non-demanders in a choice experiment", estimates the benefits of having a weed management program for a lake in Sweden, and then compares them with corresponding costs. The policy recommendation from a simple cost-benefit rule is to control the weed at some specific sites of the lake. This paper also suggest how to distinguish those that have a positive WTP for at least one of the attributes (demanders) from those that have zero WTP for all attributes (non-demanders). The advantage of the suggested approach is that it facilitates to more clearly distinguish between conditional and unconditional willingness to pay. The suggested approach could also overcome some of the problems in the literature with negative welfare measures. Article 3: "Assessing transfer errors in the benefit transfer method: An application of invasive weed management using choice experiment", tests the accuracy of transferring benefits of a weed management program from one lake to another using choice experiment. The transfer errors are assessed and the convergent validity hypothesis is tested. Estimating the accuracy of benefit transfer for weed management is policy relevant as there are a number of lakes in Sweden infested with the water weed. The convergent validity was rejected for three out of five welfare estimates with a ten per cent significance level. Article 4: "Willingness to pay for environmental improvements in hydropower regulated rivers", assesses the benefits of environmental improvements along hydropower regulated rivers using choice experiments. Remedial measures that improve the conditions for fish, benthic invertebrates and river-margin vegetation were found to have a significant welfare increasing impact. The results can be of value for the implementation of the Water Framework Directives in Sweden, which aims to reform the use of all surface water and ground water in the member states

Random Feature Maps for Dot Product Kernels

Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product kernels into linear Euclidean spaces. We base our results on a classical result in harmonic analysis characterizing all dot product kernels and use it to define randomized feature maps into explicit low dimensional Euclidean spaces in which the native dot product provides an approximation to the dot product kernel with high confidence.Comment: To appear in the proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS 2012). This version corrects a minor error with Lemma 10. Acknowledgements : Devanshu Bhimwa

Empirical Likelihood for Regression Discontinuity Design

This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity designs and treat the regression functions as nonparametric. The proposed inference procedures do not require asymptotic variance estimation and the confidence sets have natural shapes, unlike the conventional Wald-type method. These features are illustrated by simulations and an empirical example which evaluates the effect of class size on pupils' scholastic achievements. Bandwidth selection methods, higher-order properties, and extensions to incorporate additional covariates and parametric functional forms are also discussed.Empirical likelihood, Nonparametric methods, Regression discontinuity design, Treatment effect

Fiscal Policy Effects in the European Union

This paper analyzes empirically the impact of fiscal policy on the price level for the cases of Germany and Spain. We investigate whether the fiscal theory of the price level (FTPL) is able to deliver a reasonable explanation for the different performances of the price level in these two countries during recent years. We apply two different approaches. The first is a Bayesian VAR model using sign restrictions to assess the relation between surpluses and public debt. Afterwards, we use a Bayesian regime-switching model to uncover changes in monetary and fiscal policy behavior. The analysis basically shows that in each of the two countries fiscal shocks have a significant impact on the price level. Nonetheless, the FTPL does not deliver a reasonable explanation for the differences in the pattern of inflation between the two countries.Fiscal theory, policy interaction, monetary policy, public debt, price level, Euro area

A role for the developing lexicon in phonetic category acquisition

Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning