Abstract
With the development of computer science and information science, text classification technology has been greatly developed and its application scenarios have been widened. In traditional process of text classification, the existing method will lose much logical relationship information of text. The logical relationship information of a text refers to the relationship information among different logical parts of the text, such as title, abstract, and body. When human beings are reading, they will take title as an important part to remind the central idea of the article, abstract as a brief summary of the content of the article, and body as a detailed description of the article. In most of the text classification studies, researchers concern more about the relationship among words (word frequency, semantics, etc.) and neglect the logical relationship information of text. It will lose information about the relationship among different parts (title, body, etc.) and have an influence on the performance of text classification. Therefore, we propose a text classification algorithm—fusing the logical relationship information of text in neural network (FLRIOTINN), which complements the logical relationship information into text classification algorithms. Experiments show that the effect of FLRIOTINN is better than the conventional backpropagation neural networks which does not consider the logical relationship information of text.
Highlights
In recent years, with the development of science and technology, natural language processing (NLP) has been greatly developed due to its extensive application scenarios [1,2,3,4]
Text classification is an important branch of NLP. e application fields of text classification are extremely wide, such as opinion mining [5], social emotion detection [6], and educational knowledge recognition [7]
We propose fusing text logical relationship information of text in neural network (FLRIOTINN), which processes title and body, respectively
Summary
With the development of science and technology, natural language processing (NLP) has been greatly developed due to its extensive application scenarios [1,2,3,4]. In order to improve the effect of text classification, many researchers adopt the way of fusing different information in text classification. Many researchers have studied the text with obvious structure: D’hondt et al [11] emphasize the effect of phrases (versus single word) in patent and news classification by incorporating the text’s statistical phrases and linguistic phrases into the word bag model. Hu et al [12] focus on the improvement of patent keyword extraction algorithm by using distributed Skip-gram model and propose a new text classification keyword extraction method to improve the effect of the text classification algorithm. They focus more on the relationship among words of text
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