论文标题
短文分类通过术语图
Short Text Classification via Term Graph
论文作者
论文摘要
短文本分类阳离子是一种将简短句子与预先标签进行分类的方法。但是，短文本的文本长度短，这导致了稀疏特征的具有挑战性的问题。大多数现有方法将每个简短的句子视为独立且相同分布的（IID），句子本身中仅专注于句子中的本地上下文，并且丢失了句子之间的关系信息。为了克服这些局限性，我们提出了一个路径步道模型，该模型结合了图形网络的强度和简短的句子来解决短文的稀疏性。四个不同可用数据集的实验结果表明，我们的Pathwalk方法可实现最新的结果，证明了图形网络对短文本分类的效率和鲁棒性。
Short text classi cation is a method for classifying short sentence with prede ned labels. However, short text is limited in shortness in text length that leads to a challenging problem of sparse features. Most of existing methods treat each short sentences as independently and identically distributed (IID), local context only in the sentence itself is focused and the relational information between sentences are lost. To overcome these limitations, we propose a PathWalk model that combine the strength of graph networks and short sentences to solve the sparseness of short text. Experimental results on four different available datasets show that our PathWalk method achieves the state-of-the-art results, demonstrating the efficiency and robustness of graph networks for short text classification.
