An Extension of the VSM Documents Representation using Word Embedding

D. Morariu, L. Vintan, R. Cretulescu
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引用次数: 2

Abstract

Abstract In this paper, we will present experiments that try to integrate the power of Word Embedding representation in real problems for documents classification. Word Embedding is a new tendency used in the natural language processing domain that tries to represent each word from the document in a vector format. This representation embeds the semantically context in that the word occurs more frequently. We include this new representation in a classical VSM document representation and evaluate it using a learning algorithm based on the Support Vector Machine. This new added information makes the classification to be more difficult because it increases the learning time and the memory needed. The obtained results are slightly weaker comparatively with the classical VSM document representation. By adding the WE representation to the classical VSM representation we want to improve the current educational paradigm for the computer science students which is generally limited to the VSM representation.
使用词嵌入扩展VSM文档表示
在本文中,我们将展示一些实验,试图将单词嵌入表示的力量整合到文档分类的实际问题中。词嵌入是自然语言处理领域的一种新趋势,它试图以向量格式表示文档中的每个词。这种表示嵌入了语义上下文,因为单词出现的频率更高。我们将这种新的表示包含在经典的VSM文档表示中,并使用基于支持向量机的学习算法对其进行评估。这些新添加的信息使分类变得更加困难,因为它增加了学习时间和所需的记忆。所得结果与经典的VSM文档表示相比略弱。通过将WE表示添加到经典的VSM表示中,我们希望改善当前计算机科学学生的教育范式,这种范式通常仅限于VSM表示。
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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