Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained, "off-the-shelf", parts-of-speech tagging tools

This paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.

Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained, “off-the-shelf”, parts-of-speech tagging tools

his paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.

The productivity-wage and productivity-employment nexus : a panel data analysis of Indian manufacturing

This article investigates the long-run relationship between labour productivity and employment, and between labour productivity and real wages in the case of the Indian manufacturing sector. The panel data set consists of 17 two-digit manufacturing industries for the period 1973–1974 to 1999–2001. We find that productivity-wages and productivity-employment are panel cointegrated for all industries. We find that both employment and real wages exert a positive effect on labour productivity. We argue that flexible labour market has a significant influence on manufacturing productivity, employment and real wages in the case of Indian manufacturing.

A panel regression method for quantifying the convergence of house prices

The convergence among house prices has attracted much attention from researchers. Previous research mainly utilised a time-series regression method to investigate convergences of house prices, which may ignore the heterogeneity of houses across cities. This research developed a panel regression method, by which the heterogeneity of house prices can be captured. Seemingly unrelated regression estimators were also adapted to deal with the contemporary correlations across cities. Investigation of the convergence among house prices in the Australian capital cities was carried out by using the developed panel regression method. Results suggested that house prices converge in Sydney, Adelaide and Hobart but diverge in Darwin.

New housing supply and residential construction costs in Australia : a panel ECM approach

The supply of new housing in Australia has been experiencing a low increase rate since the 1990s in conjunction with an increasingly strong housing demand. On the contrary, residential construction costs across Australia?s states maintained dramatic increases simultaneously. Economic theory suggests that new housing supply is correlated to the costs of residential constructions. However, few empirical studies have focused on examining this relationship for Australian housing markets. To comprehensively investigate the relationship between the supply of new housing and residential construction costs a function for new housing supply considering the effects of regional heterogeneities is introduced in this study. By estimating a panel error correction model (ECM) applicable for quantifying the correlation with regional heterogeneities, this research identifies that a causal link and a strong correlation exist in between new housing supply and residential construction costs in Australia.

Automated classification of human daily activities in ambulatory environment

This paper presents a human daily activity classification approach based on the sensory data collected from a single tri-axial accelerometer worn on waist belt. The classification algorithm was realized to distinguish 6 different activities including standing, jumping, sitting-down, walking, running and falling through three major steps: wavelet transformation, Principle Component Analysis (PCA)-based dimensionality reduction and followed by implementing a radial basis function (RBF) kernel Support Vector Machine (SVM) classifier. Two trials were conducted to evaluate different aspects of the classification scheme. In the first trial, the classifier was trained and evaluated by using a dataset of 420 samples collected from seven subjects by using a k-fold cross-validation method. The parameters σ and c of the RBF kernel were optimized through automatic searching in terms of yielding the highest recognition accuracy and robustness. In the second trial, the generation capability of the classifier was also validated by using the dataset collected from six new subjects. The average classification rates of 95% and 93% are obtained in trials 1 and 2, respectively. The results in trial 2 show the system is also good at classifying activity signals of new subjects. It can be concluded that the collective effects of the usage of single accelerometer sensing, the setting of the accelerometer placement and efficient classifier would make this wearable sensing system more realistic and more comfortable to be implemented for long-term human activity monitoring and classification in ambulatory environment, therefore, more acceptable by users.

Reduction in corticospinal inhibition in the trained and untrained limb following unilateral leg strength training

This study used transcranial magnetic stimulation to measure the corticospinal responses following 8 weeks of unilateral leg strength training. Eighteen healthy, non-strength trained participants (14 male, 4 female; 18–35 years of age) were matched for age, gender, and pre-training strength; and assigned to a training or control group. The trained group participated in unilateral horizontal leg press strength training, progressively overloaded and wave periodised, thrice per week for 8 weeks. Testing occurred prior to the intervention, at the end of 4 weeks and at the completion of training at 8 weeks. Participants were tested in both legs for one repetition maximum strength, muscle thickness, maximal electromyography (EMG) activity, and corticospinal excitability and inhibition. No changes were observed in muscle thickness in either leg. The trained leg showed an increase in strength of 21.2% (P = 0.001) and 29.0% (P = 0.007, compared to pre-testing) whilst the untrained contralateral leg showed 17.4% (P = 0.01) and 20.4% (P = 0.004, compared to pre-testing) increases in strength at 4 and 8 weeks, respectively. EMG and corticospinal excitability did not change; however, corticospinal inhibition was significantly reduced by 17.7 ms (P = 0.003) and 17.3 ms (P = 0.001) at 4 and 8 weeks, respectively, in the trained leg, and 25.1 ms (P = 0.001) and 20.8 ms (P = 0.001) at 4 and 8 weeks, respectively, in the contralateral untrained leg. This data support the theory of corticospinal adaptations underpinning cross-education gains in the lower limbs following unilateral strength training.

Estimating the gravity model without gravity using panel data

This article examines the effects of zero trade on the estimation of the gravity model using both simulated and real data with a panel structure, which is different from the more conventional cross-sectional structure. We begin by showing that the usual log-linear estimation method can result in highly deceptive inference when some observations are zero. As an alternative approach, we suggest using the poisson fixed effects estimator. This approach eliminates the problems of zero trade, controls for heterogeneity across countries, and is shown to perform well in small samples.

Fiscal stringency and fiscal sustainability : panel evidence from the American state and local governments

Most state (and local) governments in the U.S. operate under formal fiscal rules which limit their ability to run budget deficits and resort to debt financing. A priori, one would expect to find evidence in favor of an intertemporally balanced budget, or fiscal sustainability, for these states, especially those characterized by a relatively high degree of fiscal stringency. We test this hypothesis for a panel of 47 state–local government units (1961–2006) using four budget balance definitions and several subsamples defined based on regional classifications, or presence of certain balanced budget requirements (BBRs). Our results, obtained from panel estimation techniques that allow for general forms of serial and cross-sectional dependence, suggest that a sufficient condition for “strong” sustainability is consistently satisfied for the full sample and all subsamples in relation to balances that include special funds and/or federal grants. However, we find evidence consistent with the “weak” version of sustainability for the full sample and some regional subsamples (particularly Far West dominated by California) in at least one of the two balances that exclude these items. Finally, the BBRs seem to matter only in relation to the sustainability of the more narrowly defined balances. We discuss the implications of these findings for the role of fiscal rules and federal grant policies.

Least squares asymptotics in spurious and cointegrated panel regressions with common and idiosyncratic stochastic trends

This article makes an analytical study of the effects of the presence of both common and idiosyncratic stochastic trends on the pooled least squares estimator. The results suggest that the usual result of asymptotic normality depends critically on the absence of the common stochastic trend.

The tax-spending nexus : evidence from a panel of US state–local governments

We re-examine the tax-spending nexus using a panel of 50 US state–local government units between 1963 and 1997. We find that, unlike tax revenues, expenditures adjust to revert back to a long-term equilibrium relationship. The evidence on the short-term dynamics is also consistent with the tax-and-spend hypothesis. One implication of this finding is that the size of the government at the state–local level is not determined by expenditure demand, but rather by resource supply. This is consistent with the fact that many US state and local governments operate under constitutional or legislative limitations that seek to constrain deficits.

Flexural vibration analysis of symmetric honeycomb panel with 4 sides simply supported

The free flexural vibration of symmetric honeycomb sandwich panel with 4 sides simply supported is analyzed by CPT (classical plate theory), FSDPT (first-order shear deformation plate theory) and TSDPT (third-order shear deformation plate theory). In the analysis the honeycomb core of cells is regarded equivalently as a layer of orthotropic material whose equivalent elastic parameters are determined by the modified Gibson's formula to deduce the equation of natural frequency of the sandwich panel. As shown by an example, the calculation of natural frequency of an aluminum honeycomb panel by use of TSDPT is higher accuracy than using either CPT or FSDPT.