A Novel Feature Hashing With Efficient Collision Resolution for Bag-of-Words Representation of Text Data

Bobby A. Eclarin, Arnel C. Fajardo, Ruji P. Medina
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引用次数: 2

Abstract

Text Mining is widely used in many areas transforming unstructured text data from all sources such as patients' record, social media network, insurance data, and news, among others into an invaluable source of information. The Bag Of Words (BoW) representation is a means of extracting features from text data for use in modeling. In text classification, a word in a document is assigned a weight according to its frequency and frequency between different documents; therefore, words together with their weights form the BoW. One way to solve the issue of voluminous data is to use the feature hashing method or hashing trick. However, collision is inevitable and might change the result of the whole process of feature generation and selection. Using the vector data structure, the lookup performance is improved while resolving collision and the memory usage is also efficient.
一种具有高效冲突分辨率的文本数据词袋表示特征哈希
文本挖掘被广泛应用于许多领域,将来自各种来源的非结构化文本数据(如患者记录、社交媒体网络、保险数据和新闻等)转化为宝贵的信息来源。BoW表示是一种从文本数据中提取特征以用于建模的方法。在文本分类中,根据单词在不同文档中的出现频率和出现频率,为单词分配权重;因此,字音和砝码构成弓。解决海量数据问题的一种方法是使用特征哈希方法或哈希技巧。然而,碰撞是不可避免的,并且可能会改变整个特征生成和选择过程的结果。使用向量数据结构,在解决冲突的同时提高了查找性能,并且内存使用效率也很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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