论文标题
带有文本和软传递性的索引学习
Entailment Graph Learning with Textual Entailment and Soft Transitivity
论文作者
论文摘要
键入的需要图试图从文本中学习谓词之间的需要关系，并将其建模为谓词节点之间的边缘。组成图的构建通常遭受严重的稀疏性和分布相似性的不可靠性。我们提出了一种两阶段的方法，即带有文本和传递性（EGT2）的需要图。 EGT2通过识别由键入CCG放置的谓词形成的模板句子之间可能的文本需要学习本地的关系关系。基于生成的本地图，EGT2随后使用三个新颖的软传递性约束来考虑构成结构中的逻辑传递性。基准数据集上的实验表明，EGT2可以很好地对需要图中的传递性进行模拟以减轻稀疏性问题，并导致对当前最新方法的显着改善。
Typed entailment graphs try to learn the entailment relations between predicates from text and model them as edges between predicate nodes. The construction of entailment graphs usually suffers from severe sparsity and unreliability of distributional similarity. We propose a two-stage method, Entailment Graph with Textual Entailment and Transitivity (EGT2). EGT2 learns local entailment relations by recognizing possible textual entailment between template sentences formed by typed CCG-parsed predicates. Based on the generated local graph, EGT2 then uses three novel soft transitivity constraints to consider the logical transitivity in entailment structures. Experiments on benchmark datasets show that EGT2 can well model the transitivity in entailment graph to alleviate the sparsity issue, and lead to significant improvement over current state-of-the-art methods.
