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首页> 外文会议>Pacific Rim international conference on artificial intelligence >Fast Training of a Graph Boosting for Large-Scale Text Classification
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Fast Training of a Graph Boosting for Large-Scale Text Classification

机译：图训练的快速训练，用于大规模文本分类

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This paper proposes a fast training method for graph classification based on a boosting algorithm and its application to sentimental analysis with input texts represented by graphs. Graph format is very suitable for representing texts structured with Natural Language Processing techniques such as morphological analysis, Named Entity Recognition, and parsing. A number of classification methods which represent texts as graphs have been proposed so far. However, many of them limit candidate features in advance because of quite large size of feature space. Instead of limiting search space in advance, we propose two approximation methods for learning of graph-based rules in a boosting. Experimental results on a sentimental analysis dataset show that our method contributes to improved training speed. In addition, the graph representation-based classification method exploits rich structural information of texts, which is impossible to be detected when using other simpler input formats, and shows higher accuracy.
机译：提出了一种基于提升算法的图分类快速训练方法，并将其应用于图表示输入文本的情感分析。图形格式非常适合表示使用自然语言处理技术（例如形态分析，命名实体识别和解析）构造的文本。迄今为止，已经提出了许多将文本表示为图形的分类方法。但是，由于特征空间很大，它们中的许多预先限制了候选特征。代替预先限制搜索空间，我们提出了两种近似方法来学习基于图的规则。在情感分析数据集上的实验结果表明，我们的方法有助于提高训练速度。另外，基于图形表示的分类方法利用了丰富的文本结构信息，这在使用其他更简单的输入格式时是无法检测到的，并且显示出更高的准确性。

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