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首页> 外文期刊>International Journal of Innovative Computing Information and Control >ENHANCING TEXT REPRESENTATION FOR CLASSIFICATION TASKS WITH SEMANTIC GRAPH STRUCTURES
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ENHANCING TEXT REPRESENTATION FOR CLASSIFICATION TASKS WITH SEMANTIC GRAPH STRUCTURES

机译：增强具有语义图结构的分类任务的文本表示

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摘要

To represent the textual knowledge more expressively, a kind of semantic-based graph structure is proposed, in which more semantic and ordering information among terms as well as the structural information of the text are incorporated. Such model can be constructed by extracting representative terms from texts and their mutually semantic relationships. Afterward, it is represented as a graph, whose nodes are the selected terms and whose edges are the corresponding relationships respectively. Moreover, the weight is assigned to each edge so that the strength of relationship between two terms can be measured. Furthermore, for this weighted directed graph structure, a novel graph similarity algorithm is developed by extracting the maximum common subgraph between two concerned graphs, which can therefore be used to measure the distance between two graph structures, i.e., two texts, and further be applied to classification tasks. Finally, some experiments have been conducted with the Chinese benchmark corpus for validation. The experimental results have proved the better performance of the proposed textual knowledge representation model in terms of its precision and recall.
机译：为了更好地表达文本知识，提出了一种基于语义的图结构，其中结合了术语之间的更多语义和顺序信息以及文本的结构信息。可以通过从文本及其相互语义关系中提取代表性术语来构建这种模型。之后，将其表示为图，其节点为所选术语，其边分别为对应关系。此外，将权重分配给每个边缘，以便可以测量两个项之间的关系强度。此外，对于这种加权有向图结构，通过提取两个相关图之间的最大公共子图，开发了一种新颖的图相似性算法，因此可以用于测量两个图结构（即两个文本）之间的距离，并进一步应用。分类任务。最后，对中国基准语料库进行了一些实验以进行验证。实验结果证明了所提出的文本知识表示模型在精度和召回性方面具有更好的性能。

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