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Chapter and Conference Paper
Nonlinear Hydrological Time Series Forecasting Based on the Relevance Vector Regression
As long leading-time hydrological forecast is a complex non-linear procedure, traditional methods are easy to get slow convergence and low efficiency. The basic relevance vector machine (BRVM) and the develope...
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Chapter and Conference Paper
Biased Wavelet Neural Network and Its Application to Streamflow Forecast
Long leading-time streamflow forecast is a complex non-linear procedure. Traditional methods are easy to get slow convergence and low efficiency. The biased wavelet neural network (BWNN) based on BP learning a...
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Chapter and Conference Paper
Domain Adaptation for Conditional Random Fields
Conditional Random Fields (CRFs) have received a great amount of attentions in many fields and achieved good results. However, a case frequently encountered in practice is that the test data’s domain is differ...
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Chapter and Conference Paper
An Ontology-Based Collaborative Design System
A collaborative design system architecture based on ontology is proposed. In the architecture, OWL is used to construct global shared ontology and local ontology; both of them are machine-interpretable. The fo...
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Chapter and Conference Paper
Study of Collaborative Design Based on Fuzzy Theory
Under the Ontology-based collaborative design framework constructed by OWL and SWRL, in order to further extend the expression and reasoning abilities of domain knowledge description and to achieve the reasoni...
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Chapter and Conference Paper
Mining Uncertain Sentences with Multiple Instance Learning
Distinguishing uncertain information from factual ones in online texts is of essential importance in information extraction, because uncertain information would mislead systems to find useless even fault infor...
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Chapter and Conference Paper
Labelwise Margin Maximization for Sequence Labeling
In sequence labeling problems, the objective functions of most learning algorithms are usually inconsistent with evaluation measures, such as Hamming loss. In this paper, we propose an online learning algorith...
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Chapter and Conference Paper
An Effective Feature Selection Method for Text Categorization
Feature selection is an efficient strategy to reduce the dimensionality of data and removing the noise in text categorization. However, most feature selection methods aim to remove non-informative features bas...
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Chapter and Conference Paper
Chinese Word Segmentation with Character Abstraction
Chinese word segmentation is an important and necessary problem to analyze Chinese texts. In this paper, we focus on the primary challenges in Chinese word segmentation: low accuracy of out-of-vocabulary word....
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Chapter and Conference Paper
Online Distributed Passive-Aggressive Algorithm for Structured Learning
The training phase is time-consuming for structured learning, especially for supper-tagging tasks. In this paper, we propose an online distributed Passive-Aggression (PA) by averaging parameters for parallel t...
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Chapter and Conference Paper
Improving Multi-pass Transition-Based Dependency Parsing Using Enhanced Shift Actions
In multi-pass transition-based dependency parsing algorithm, the shift actions are usually inconsistent for the same node pair in different passes. Some node pairs have a indeed dependency relation, but the modif...
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Chapter and Conference Paper
Text Classification with Document Embeddings
Distributed representations have gained a lot of interests in natural language processing community. In this paper, we propose a method to learn document embedding with neural network architecture for text cla...
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Chapter and Conference Paper
Overview of the NLPCC 2015 Shared Task: Chinese Word Segmentation and POS Tagging for Micro-blog Texts
In this paper, we give an overview for the shared task at the 4th CCF Conference on Natural Language Processing & Chinese Computing (NLPCC 2015): Chinese word segmentation and part-of-speech (POS) tagging for ...
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Chapter and Conference Paper
Learning to Rank Answers for Definitional Question Answering
In definitional question answering (QA), it is essential to rank the candidate answers. In this paper, we propose an online learning algorithm, which dynamically construct the supervisor to reduce the adverse ...
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Chapter and Conference Paper
Application of Big Data Processing Technology in the Intelligent Network Management System
In this paper, according to the requirements of China Telecom’s Network Planning and intelligent platform construction technology, we describe how to use big data technology to improve the processing efficienc...
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Chapter and Conference Paper
Transition-Based Dependency Parsing with Long Distance Collocations
Long distance dependency relation is one of the main challenges for the state-of-the-art transition-based dependency parsing algorithms. In this paper, we propose a method to improve the performance of transit...
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Chapter and Conference Paper
Convolutional Deep Neural Networks for Document-Based Question Answering
Document-based Question Answering aims to compute the similarity or relevance between two texts: question and answer. It is a typical and core task and considered as a touchstone of natural language understand...
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Chapter and Conference Paper
Overview of the NLPCC-ICCPOL 2016 Shared Task: Chinese Word Segmentation for Micro-Blog Texts
In this paper, we give an overview for the shared task at the 5th CCF Conference on Natural Language Processing & Chinese Computing (NLPCC 2016): Chinese word segmentation for micro-blog texts. Different with ...
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Chapter and Conference Paper
End-to-End Neural Text Classification for Tibetan
As a minority language, Tibetan has received relatively little attention in the field of natural language processing (NLP), especially in current various neural network models. In this paper, we investigate th...
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Article
Robust and reliable estimation via recursive nonlinear dynamic data reconciliation based on cubature Kalman filter
Since measurements of process variables are subject to measurements errors as well as process variability, data reconciliation is the procedure of optimally adjusting measured date so that the adjusted values ...