论文标题
Synwmd：语法感知单词Mover的句子相似性评估的距离
SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation
论文作者
论文摘要
Word Mover的距离（WMD）计算单词和模型之间的距离文本相似性与两个文本序列中单词之间的移动成本相似。但是，它在句子相似性评估中不能提供良好的性能，因为它不包含单词重要性，并且在句子中未能将固有的上下文和结构信息纳入句子。提出了一种改进的WMD方法，称为语法解析树，称为语法感知单词Mover的距离（SYNWMD），以解决这项工作中的这两个缺点。首先，基于从句子的句法树木中提取的一词共发生统计量建立了加权图。每个单词的重要性是从图形连接性推断出的。其次，在计算单词之间的距离时，考虑了单词的局部句法解析结构。为了证明拟议的SynWMD的有效性，我们对6个文本语义相似性（STS）数据集和4个句子分类数据集进行了实验。实验结果表明，SynWMD在STS任务上实现了最先进的性能。它还在句子分类任务上胜过其他基于WMD的方法。
Word Mover's Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good performance in sentence similarity evaluation since it does not incorporate word importance and fails to take inherent contextual and structural information in a sentence into account. An improved WMD method using the syntactic parse tree, called Syntax-aware Word Mover's Distance (SynWMD), is proposed to address these two shortcomings in this work. First, a weighted graph is built upon the word co-occurrence statistics extracted from the syntactic parse trees of sentences. The importance of each word is inferred from graph connectivities. Second, the local syntactic parsing structure of words is considered in computing the distance between words. To demonstrate the effectiveness of the proposed SynWMD, we conduct experiments on 6 textual semantic similarity (STS) datasets and 4 sentence classification datasets. Experimental results show that SynWMD achieves state-of-the-art performance on STS tasks. It also outperforms other WMD-based methods on sentence classification tasks.
