Neural networks provide new possibilities to uncover semantic relationships between words by involving contextual information, and further a way to learn the matching pattern from document-query word contextual similarity matrix, which has brought promising results in IR. However, most neural IR methods rely on the conventional word-word matching framework for finding a relevant document for a query. Its effect is limited due to the wide gap between the lengths of query and document. To address this problem, we propose a salient context-based semantic matching (SCSM) method to build a bridge between query and document. Our method locates the most relevant context in the document using a shifting window with adapted length and then calculates the relevance score within it as the representation of the document. We define the notion of contextual salience and the corresponding measures to calculate the relevance of a context to a given query, in which the interaction between the query and the context is modeled by semantic similarity. Experiments on various collections from TREC show the effectiveness of our model as compared to the state-of-the-art methods.

神经网络为通过涉及上下文信息来揭示单词之间的语义关系提供了新的可能性，并且为从文档查询单词上下文相似性矩阵中学习匹配模式提供了一种新方法，这在IR中带来了可喜的结果。但是，大多数神经IR方法依赖于常规的单词-单词匹配框架来查找相关文档以进行查询。由于查询和文档的长度之间的巨大差距，其效果受到限制。为了解决这个问题，我们提出了一种基于上下文的显着语义匹配（SCSM）方法，以在查询和文档之间建立桥梁。我们的方法是使用具有适当长度的移动窗口在文档中找到最相关的上下文，然后计算其中的相关性得分作为文档的表示形式。我们定义了上下文显着性的概念和相应的度量，以计算上下文与给定查询的相关性，其中查询和上下文之间的交互通过语义相似性进行建模。来自TREC的各种集合的实验表明，与最新方法相比，我们模型的有效性。