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Recent work on neural network models shows success in dependency parsing. In this paper, we present a sequence learning dependency parsing (SLDP) model using long short-term memory for shift-reduce parser. A feed-forward neural network is used to build greedy model from rich local features. With the features extracted by the local model, we further train a long short-term memory (LSTM) model optimized...
Recently, the deep learning based methods, especially the ones based on convolutional neural network (CNN), achieved remarkable progresses in sentiment analysis. However, the CNN based methods do not take the latent topic in text into consideration. In this paper, we propose a CNN based Diversified Restrict Boltzmann Machine (RBM) method to model the sequence level latent topics in the sentences for...
The investors in financial market have shown great concerns in the events that may cause fluctuations in the capital market. Traditional event detection and type recognition methods were majorly based on text processing techniques while few research considers the financial time-series features. As we know, there are large amount of financial time-series data available such as stock transaction data...
A novel multiscale phase congruency (MPC) based analysis method is proposed in this paper for edge saliency detection and non-salient region texture suppression. Several MPC maps are proposed to be merged. Gaussian function based center priors and threshold processing are applied for the final edge saliency map generation, which can effectively suppress the textures and the detailed edges of non-salient...
The performance of a statistical machine translation (SMT) system heavily depends on the quantity and quality of the bilingual language resource. However, the pervious work mainly focuses on the quantity and tries to collect more bilingual data. In this paper, we aim to optimize the bilingual corpus to improve the performance of the translation system. We propose methods to process the bilingual language...
Inner structures of Chinese lexical concepts have embedded some useful semantic relations. In this paper, we proposed a new statistical approach to mine lexical hyponymy relations from large-scale concept set, instead of analyzing inner structures. Firstly we designed common suffix tree to cluster the lexical concept set. Class concepts are then extracted by statistic-base rules we investigated in...
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