Extraction of sentiment topic sentences of Chinese texts

Fan, Na; Cai, Weiquan; Yu, Zhiwu; Li, Huixian

doi:10.3724/sp.j.1087.2009.01171

Cited by 2 publications

(3 citation statements)

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“…The comparison of feature vectors between an entity pair is used to determine the semantic relation. This can be achieved by employing various learning models such as the maximum entropy 8 , support vector machine (SVM) 9 , and so on. Xi et al proposed a method for extracting semantic relations based on combining various linguistic features, including lexical, syntactic, and semantic features, in their publication 10 .…”

Section: Feature-based Methodsmentioning

confidence: 99%

“…In equation (8), if the value is 1, the entity i E is in the early half of the CI; if the value is 0, it is in the latter half.…”

Section: Traditional Featuresmentioning

confidence: 99%

“…The combination feature of the entity comprises lexical, phrase, order, and property characteristics. Hence, the equation ( 10) is established for the vector of entity i E , taking into account equations (1), ( 6), (8), and (9). } , , , {…”

Section: Combination Featurementioning

confidence: 99%

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A people-item relation extraction method based on multiple kernel support vector machine model

Gao,

Liu

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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In this research, we present a novel approach for relation extraction using the multiple kernel support vector machine model. The aim is to improve the comprehension of Chinese Instructions by family service robots. Our approach focuses on extracting the people-item relation from Chinese instructions. We start by defining four categories of people-item relations: sequential, belong to, equivalent, and direction. Next, we construct a feature combination for the entity using lexical, phrase, order, and property features. We then generate multiple kernel functions using a weighted sum method and selected foundation kernel functions (lexical, syntactic, and property). The multiple kernel support vector machine model is constructed using a simple multiple kernel learning technique. The experimental findings validate that our proposed approach outperforms current methodologies in terms of accuracy, retrieval rate, and F-measure.

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Section: Feature-based Methodsmentioning

confidence: 99%

“…In equation (8), if the value is 1, the entity i E is in the early half of the CI; if the value is 0, it is in the latter half.…”

Section: Traditional Featuresmentioning

confidence: 99%