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Because of their fuzzy and non-stationary nature, financial time series forecasting is still a challenge. In this paper, we propose and implement a hybrid model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The approach contains three steps: feature and time alignment in data...
This paper addresses the issue of web information extraction to support automatic teacher information management. We propose an effective approach based on block segmentation. First, the teacher introduction web pages are divided into independent blocks, where html tags and punctuation marks are used as segmentation criterion. Then CRF model is employed to label the text. We apply this approach on...
Conditional random fields (CRFs) have been used for many sequence labeling tasks and got excellent results. Further, the supervised model strongly depends on the huge training data. Active learning is a different way rather than relying on a large amount random sampling. However, random sampling constructively participates in the optimal choosing training examples. Based on different query strategies,...
Unknown word recognition is a very important problem in natural language processing. It has a great influence on the performance of dictionary construction and word segmentation. This paper introduces two methods to improve the effect of Chinese unknown word recognition by using Conditional Random Fields: the rough label of the characters and the N-best listing. The CRF with the two methods proposed...
This paper presents a new Chinese chunking algorithm based on conditional random fields. Conditional random fields overcome the label bias problem, model the labeling sequence and utilize many types of features. Furthermore, an algorithm of chunk similarity computation is proposed based on the systematic similarity method and semantic dictionary. The experimental results show that this approach achieves...
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