Make Word Sense Disambiguation in EBMT Practical

PACLIC 20 / Wuhan, China / 1-3 November, 200

Image Completion Based on Edge Prediction and Improved Generator

The existing image completion algorithms may result in problems of poor completion in the missing parts, excessive smoothing or chaotic structure of the completed areas, and large training cycle when processing more complex images. Therefore, a two-stage adversarial image completion model based on edge prediction and improved generator structure has been put forward to solve the existing problems. Firstly, Canny edge detection is utilized to extract the damaged edge image, to predict and to complete the edge information of the missing area of the image in the edge prediction network. Secondly, the predicted edge image is taken as a priori information by the Image completion network to complete the damaged area of the image, so as to make the structure information of the completed area more accurate. A-JPU module is designed to ensure the completion result and speed up training for existing models due to the enormous number of computations caused by the large use of extended convolution in the self-coding structure. Finally, the experimental results on Places 2 dataset show that the PSNR and SSIM of the image using the image completion model are higher and the subjective visual effect is closer to the real image than some other image completion models

Littoral Slope, Water Depth and Alternative Response Strategies to Light Attenuation Shape the Distribution of Submerged Macrophytes in a Mesotrophic Lake

Light is a major limiting resource in aquatic ecosystems and numerous studies have investigated the response of submerged macrophytes to low light conditions. However, few studies have tested whether different light response strategies can also have consequences for macrophyte distribution along different littoral slopes in lakes, which are known to affect macrophyte biomass due to differences in drag forces and sediment characteristic. In this study, we tested (1) whether two macrophyte species of different growth forms (canopy-forming: Potamogeton maackianus, rosette-type: Vallisneria natans) differ in their response strategies to low light conditions and (2) how these responses influence their distribution along different basin slopes in the mesotrophic Lake Erhai, China. We hypothesized that the canopy-forming species responds to low light conditions at deeper sites by stem elongation while the rosette-type species increases its shoot chlorophyll content. As a consequence, P. maackianus should have a higher susceptibility to drag forces and thus prevail at sites with lower slopes. Sites with higher slopes should offer a niche for rosette-type species like V. natans that can better withstand drag forces. We surveyed the distribution and abundance of the two macrophyte species at 527 sampling points along 97 transects in Lake Erhai and measured their height, leaf and stem/rhizome biomass, and leaf chlorophyll a content at different water depths. Our results confirmed stem elongation as a strategy to low light conditions by the canopy-forming species P. maackianus, while V. natans produced more chlorophyll a per shoot biomass at deeper sites to tolerate shading. As hypothesized, these alternative response strategies to low light conditions resulted in a trade-off regarding the plants ability to grow at different basin slopes. P. maackianus was dominant at sites with low-moderate slope (0–4%) and low-moderate water depth (2–4 m), while sites with high basin slope (4–7%) combined with moderate-high water depth (3–5 m) were dominantly colonized by V. natans. The latter habitat thus represents a potential refuge for rosette-type macrophyte species that are often outcompeted when shading increases during eutrophication

Improving PinYin to Chinese Conversion With a Whole Sentence . . .

We address the problem of statistical language modeling in the context of PinYin to Chinese (PTC) conversion, a similar problem to speech recognition but without acoustic recognition step. Inputted phonetic syllables were first segmented and converted into word lattice, which was then scored within a Source-Channel framework in order to find the most probable Chinese sentence. In particular, we discuss the use of a Whole Sentence Maximum Entropy (WSME) model, an expressive framework for constructing language models with diverse features. Experiment showed WSME model trained with d2-ngrams and word triggers achieved a 20% reduction in perplexity and a 11.05% reduction in character conversion error over a baseline trigram

Filtering Junk Mail with A Maximum Entropy Model

The task of junk mail filtering is to rule out unsolicited bulk e-mail (junk) automatically from a user's mail stream. Two classes of methods have been shown to be useful for classifying e-mail messages. The rule based method uses a set of heuristic rules to classify e-mail messages while the statistical based approach models the di#erence of messages statistically, usually under a machine learning framework. Generally speaking, the statistical based methods are found to outperform the rule based method, yet we found, by combining different kinds of evidence used in the two approaches into a single statistical model, further improvement can be obtained