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81.
In text categorization, it is quite often that the numbers of documents in different categories are different, i.e., the class distribution is imbalanced. We propose a unique approach to improve text categorization under class imbalance by exploiting the semantic context in text documents. Specifically, we generate new samples of rare classes (categories with relatively small amount of training data) by using global semantic information of classes represented by probabilistic topic models. In this way, the numbers of samples in different categories can become more balanced and the performance of text categorization can be improved using this transformed data set. Indeed, the proposed method is different from traditional re-sampling methods, which try to balance the number of documents in different classes by re-sampling the documents in rare classes. Such re-sampling methods can cause overfitting. Another benefit of our approach is the effective handling of noisy samples. Since all the new samples are generated by topic models, the impact of noisy samples is dramatically reduced. Finally, as demonstrated by the experimental results, the proposed methods can achieve better performance under class imbalance and is more tolerant to noisy samples. 相似文献
82.
A challenge for sentence categorization and novelty mining is to detect not only when text is relevant to the user’s information need, but also when it contains something new which the user has not seen before. It involves two tasks that need to be solved. The first is identifying relevant sentences (categorization) and the second is identifying new information from those relevant sentences (novelty mining). Many previous studies of relevant sentence retrieval and novelty mining have been conducted on the English language, but few papers have addressed the problem of multilingual sentence categorization and novelty mining. This is an important issue in global business environments, where mining knowledge from text in a single language is not sufficient. In this paper, we perform the first task by categorizing Malay and Chinese sentences, then comparing their performances with that of English. Thereafter, we conduct novelty mining to identify the sentences with new information. Experimental results on TREC 2004 Novelty Track data show similar categorization performance on Malay and English sentences, which greatly outperform Chinese. In the second task, it is observed that we can achieve similar novelty mining results for all three languages, which indicates that our algorithm is suitable for novelty mining of multilingual sentences. In addition, after benchmarking our results with novelty mining without categorization, it is learnt that categorization is necessary for the successful performance of novelty mining. 相似文献
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词汇之间的搭配关系应该是具有一定的规则性,还是开放而不完全拘泥于规则呢？除了基本的语法规则和语义的限制外,其他因素是否会影响词汇之间的搭配呢？本文针对以上的课题,运用认知语言学的相关理论,探析了英语词汇搭配的认知规律,并阐释了经验主义、概念范畴化和概念结构对词汇搭配的制约作用。 相似文献
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刘齐宣 《玉溪师范学院学报》2001,17(1):74-76
我国英语学习者心理范畴化是其英语心理发生和发展的重要方面.学习者通过种种课堂经验所产生的类的意识是此种心理范畴化的意识基础.这要求教师按照英语心理范畴化的基本要求组织教学内容来为学习者提供合理的课堂经验. 相似文献
86.
Hierarchical Text Categorization Using Neural Networks 总被引：8，自引：1，他引：7
This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks. 相似文献
87.
王细娇 《宁波广播电视大学学报》2006,4(1):33-34,84
本文从认知语言学最有影响的原型理论和基本范畴理论角度论证了基本等级范畴词汇的重要性,并因此提出要加强基本范畴词汇的教学,认为词汇意义的讲解应以原型意义为中心,重视词语多义、多用之间的深层联系——词语隐喻和转喻的学习。 相似文献
88.
基于模糊分类规则树的文本分类 总被引：2，自引：0，他引：2
针对传统的基于关联规则的文本分类方法在分类文本时需要遍历分类器中的所有规则,分类效率非常低的问题,提出一种基于模糊分类规则树（FCR-tree）的文本分类方法.分类器中的规则以树的形式存储,由于树型结构避免了重复结点的存储,节省了存储空间.模糊分类关联规则与一般分类规则相比,不仅包含了词条信息,还包含了词条出现频度对应的模糊集,所以FCR-tree的构建过程及树的结构不同于一般规则树CR-tree.为降低构建及遍历FCR-tree的难度,采用了构造多棵k-FCR-tree的方法.在搜索规则树时,如果结点中的词条没在待分类文本中出现,则不需要再搜索该结点引导的子树,大大减少了需要匹配的规则的数量.实验表明该方法是可行的,与遍历分类器的分类方法相比,分类效率有了明显提高. 相似文献
89.
Most previous works of feature selection emphasized only the reduction of high dimensionality of the feature space. But in cases where many features are highly redundant with each other, we must utilize other means, for example, more complex dependence models such as Bayesian network classifiers. In this paper, we introduce a new information gain and divergence-based feature selection method for statistical machine learning-based text categorization without relying on more complex dependence models. Our feature selection method strives to reduce redundancy between features while maintaining information gain in selecting appropriate features for text categorization. Empirical results are given on a number of dataset, showing that our feature selection method is more effective than Koller and Sahami’s method [Koller, D., & Sahami, M. (1996). Toward optimal feature selection. In Proceedings of ICML-96, 13th international conference on machine learning], which is one of greedy feature selection methods, and conventional information gain which is commonly used in feature selection for text categorization. Moreover, our feature selection method sometimes produces more improvements of conventional machine learning algorithms over support vector machines which are known to give the best classification accuracy. 相似文献
90.
This paper explores the incorporation of prior knowledge into support vector machines as a means of compensating for a shortage of training data in text categorization. The prior knowledge about transformation invariance is generated by a virtual document method. The method applies a simple transformation to documents, i.e., making virtual documents by combining relevant document pairs for a topic in the training set. The virtual document thus created not only is expected to preserve the topic, but even improve the topical representation by exploiting relevant terms that are not given high importance in individual real documents. Artificially generated documents result in the change in the distribution of training data without the randomization. Experiments with support vector machines based on linear, polynomial and radial-basis function kernels showed the effectiveness on Reuters-21578 set for the topics with a small number of relevant documents. The proposed method achieved 131%, 34%, 12% improvements in micro-averaged F1 for 25, 46, and 58 topics with less than 10, 30, and 50 relevant documents in learning, respectively. The result analysis indicates that incorporating virtual documents contributes to a steady improvement on the performance. 相似文献