This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
A more recent bootstrapping approach is described in @cite_23 . This algorithm requires a small number of training examples to serve as a seed. There are a variety of options discussed for automatically selecting seeds; one is to identify collocations that uniquely distinguish between senses. For plant , the collocations manufacturing plant and living plant make such a distinction. Based on 106 examples of manufacturing plant and 82 examples of living plant this algorithm is able to distinguish between two senses of plant for 7,350 examples with 97 percent accuracy. Experiments with 11 other words using collocation seeds result in an average accuracy of 96 percent.
@cite_23
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This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96 . Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and reduces the state space to be verified compared to the open-loop case. To be applied, it needs the controller and the plant formal models to be coupled. There are approaches for controller synthesis, but little has been done regarding plant model construction. While manual plant modeling is time consuming and error-prone, discretizing a simulation model of the plant leads to state excess. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples, or traces, and temporal properties. The proposed method, which is based on the translation of the problem to the Boolean satisfiability problem, is evaluated and shown to be applicable on several case study plant model synthesis tasks and on randomly generated problem instances. We examine the potential for geographic smoothing of solar photovoltaic (PV) electricity generation using 13 months of observed power production from utility-scale plants in Gujarat, India. To our knowledge, this is the first published analysis of geographic smoothing of solar PV using actual generation data at high time resolution from utility-scale solar PV plants. We use geographic correlation and Fourier transform estimates of the power spectral density (PSD) to characterize the observed variability of operating solar PV plants as a function of time scale. Most plants show a spectrum that is linear in the log–log domain at high frequencies f, ranging from to (slopes of −1.23 and −1.56), thus exhibiting more relative variability at high frequencies than exhibited by wind plants. PSDs for large PV plants have a steeper slope than those for small plants, hence more smoothing at short time scales. Interconnecting 20 Gujarat plants yields a spectrum, reducing fluctuations at frequencies corresponding to 6 h and 1 h by 23 and 45 , respectively. Half of this smoothing can be obtained through connecting 4–5 plants; reaching marginal improvement of 1 per added plant occurs at 12–14 plants. The largest plant (322 MW) showed an spectrum. This suggests that in Gujarat the potential for smoothing is limited to that obtained by one large plant. Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. It is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. Several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. Effective features in the classification of corn and weeds were analyzed using stepwise discriminant analysis. Among the seven features used in the analysis, four were sufficient to classify the two target groups of weeds and corn. These shape features were fed to artificial neural networks to discriminate between the weeds and the main crop. 180 images consisting of corn plants and four species of common weeds were collected from normal conditions of the field. Results showed that this technique was able to distinguish corn plants with an accuracy of 100 while at most 4 of the weeds were incorrectly classified as corn. In the final stage, the position of the main crop was also approximated and its accuracy was measured with respect to the real position of the crop. The position of the crop is necessary for the weeding machine to root up all of the plants except the main crop. It was concluded that the high accuracy of this method is due to the significant difference between corn and weeds in the critical period of weeding in the region.