论文标题
互动文本图挖掘带有基于序的对话框引擎
Interactive Text Graph Mining with a Prolog-based Dialog Engine
论文作者
论文摘要
除了基于神经网络的依赖性解析器和基于图的自然语言处理模块之外，我们设计了基于序言的对话框引擎，该引擎可以交互探索从文本文档中提取的排名的事实数据库。 我们重组依赖图以关注句子的最相关内容元素，并将句子标识符作为图节点集成。 此外，在对图进行排名之后，我们利用了依赖关系链接和WordNet带来的隐式语义信息的优势，其形式是主题 - 动物对象，IS-A和部分关系。 Dialog Engine从事序言事实及其推断的后果，专门针对查询的文本图，并以交互方式揭示了文档的最相关内容元素。 集成系统的开源代码可从https://github.com/ptarau/deeprank获得。 在逻辑编程（TPLP）的理论和实践中考虑的。
On top of a neural network-based dependency parser and a graph-based natural language processing module we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes. Additionally, after ranking the graph we take advantage of the implicit semantic information that dependency links and WordNet bring in the form of subject-verb-object, is-a and part-of relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank . Under consideration in Theory and Practice of Logic Programming (TPLP).
