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This paper proposes a word-by-word model selection approach to domain adaptation for Word Sense Disambiguation. By this approach, the model for a target word is automatically selected from a candidate model set, which is comprised of improved self-training models and a supervised model. The improved self-training uses sense priors to prevent its iteration from converging into undesirable states. Experimental...
The logistic regression model has achieved success in spam filtering. But it is disadvantaged by the equal adjustment of the feature weights appeared in both spam messages and ham ones during training period. This paper presents an improved logistic regression model which reduces the impact of the features appearing in both spam messages and ham ones. Byte level n-grams are employed to extract the...
This paper presents an overview of learning to rank. It includes three parts: related concepts including the definitions of ranking and learning to rank; a summary of pointwise models, pairwise models, and listwise models; estimation measures such as Normalized Discount Cumulative Gain and Mean Average Precision, respectively. Considering the deficiency that current learning to rank models lack of...
Machine transliteration is to translate the proper nouns in the source language according to its pronunciation into the target language. Recent orthographical based approach has improved the performance of machine translation significantly. Focusing on the transliteration unit alignment that provides a fundamental parameter for the model, this paper adopts a semi-supervised EMD approach-applying discriminative...
This paper describes a simple but effective discriminative learner for spam filter. We statically derive the features within Bayesian framework, cluster them into groups according to their position and then assigning weights respectively. The model is evaluated by TREC Spam corpus and compared with the TREC results. Experimental results show that our linear discriminative model can produce competitive...
Natural language is an easy and effective medium for spatial description. Thus, we foresee the emergence of methods to extract spatial relationships from text as constraints on layout of objects in a 3D scene. This would allow users to quickly display objects in 3D scenes without having to touch a desktop window-oriented interface, acquire artistic skills, or even understand nature language. The aim...
Reinforcement learning is a powerful method for solving sequential decision making problems. But it is difficult to apply to practical problems such as multi-agent systems with continuous state space problems. In this paper we present a cooperative strategy learning method to solve the problem. It combines WoLF-PHC algorithms with function approximation of RL techniques. By this method an agent could...
This paper investigates how multiple sets of attributes can be generated using a rough sets-based inductive learning method and how they can be combined for improving classification decisions, particularly in the context of text categorization, by using Dempster's rule of combination. We first propose a boosting-like technique for generating multiple sets of attributes based on rough set theory, and...
Text classification is becoming one of the key techniques in organizing and handling a large amount of text data. In this paper, a combination of ontology with statistical method is presented to improve the precision of text classification. In this study, first, different kind of linguistic ontology knowledge will be respectively acquired by learning training corpus to determine text classifiers....
Due to the complexity and flexibility of natural language, automatic linguistic knowledge acquisition and its application research becomes difficult. In this paper, we present a machine learning method to automatically acquire Chinese linguistic ontology knowledge from typical corpus. This study, first, defined the description frame of Chinese linguistic ontology knowledge, and then, automatically...
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