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.
翻译:Word Moler 的距离( Word Moler 距离) 计算单词和模型文本之间的距离, 与两个文本序列中单词之间的移动成本相近。 然而, 它在句子相似性评价中并没有很好地表现, 因为它没有包含单词重要性, 也没有在句子中考虑到内在的背景和结构信息 。 提议采用一种改良的大规模毁灭性武器方法, 使用语法剖析树, 叫做 语法觉Word Moler 的距离( Synmet), 来解决这项工作中的这两个缺点 。 首先, 以从句子合成分析树中提取的单词共振统计为基础, 每一个单词的重要性都来自图形连接性 。 其次, 在计算单词之间的距离时会考虑到本地的词的合成对等结构 。 为了证明拟议的 SyNM 的实效, 我们实验了 6 个文字语义语义相似性数据集 和 4 句子分类数据集 。 实验结果显示 Synmace 实现了 STS 任务中的状态- 。 它也超越了其他基于 句项任务 的方法 。