从文本中自动提取主语、动作、宾语和状语维度，支持自然语言查询。
Extracting Abstraction Dimensions by Identifying Syntax Pattern from Texts
- 基于语法模式识别，构建四维抽象树结构。
- 四类维度的平均精确率、召回率和F1均超80%。
- 适合需要精准文本检索与自然语言查询的场景。
本文提出一种自动从文本中发现主语维度、动作维度、宾语维度和状语维度的方法，以高效操作文本并支持自然语言查询。所构建的抽象树具备高质量：能完整表示文本中所有主语、动作、宾语及其子类关系；具备独立性：各树间无冗余表示；具备表达力：绝大多数句子可由任一树访问，其余句子至少可被一个树覆盖，使基于树的检索机制能支持自然语言查询。实验表明，基于主语、动作、宾语和状语子类关系构建的抽象树，平均精确率、召回率和F1值均超过80%。将该方法应用于自然语言查询，结果显示针对主语或宾语的不同问题模式具有高文本覆盖率；根据问题模式在主语、动作、宾语和状语四个维度上搜索多个树，可快速缩小搜索空间，准确定位目标句子，实现对文本的精确操作。
原文摘要 · Abstract (English)
This paper proposed an approach to automatically discovering subject dimension, action dimension, object dimension and adverbial dimension from texts to efficiently operate texts and support query in natural language. The high quality of trees guarantees that all subjects, actions, objects and adverbials and their subclass relations within texts can be represented. The independency of trees ensures that there is no redundant representation between trees. The expressiveness of trees ensures that the majority of sentences can be accessed from each tree and the rest of sentences can be accessed from at least one tree so that the tree-based search mechanism can support querying in natural language. Experiments show that the average precision, recall and F1-score of the abstraction trees constructed by the subclass relations of subject, action, object and adverbial are all greater than 80%. The application of the proposed approach to supporting query in natural language demonstrates that different types of question patterns for querying subject or object have high coverage of texts, and searching multiple trees on subject, action, object and adverbial according to the question pattern can quickly reduce search space to locate target sentences, which can support precise operation on texts.
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