The task of Information Extraction (IE) involves automatically converting
unstructured textual content into structured data. Most research in this field
concentrates on extracting all facts or a specific set of relationships from
documents. In this paper, we present a method for the extraction and
categorisation of an unrestricted set of relationships from text. Our method
relies on morpho-syntactic extraction patterns obtained by a distant
supervision method, and creates Syntactic and Semantic Indices to extract and
classify candidate graphs. We evaluate our approach on six datasets built on
Wikidata and Wikipedia. The evaluation shows that our approach can achieve
Precision scores of up to 0.85, but with lower Recall and F1 scores. Our
approach allows to quickly create rule-based systems for Information Extraction
and to build annotated datasets to train machine-learning and deep-learning
based classifiers.

信息抽取任务涉及将非结构化文本内容自动转换为结构化数据。本文提出了一种从文本中提取和分类无限制关系集的方法，它基于远程监督方法获取的形态 - 句法抽取模式，并创建句法和语义索引来提取和分类候选图。我们在构建在 Wikidata 和 Wikipedia 上的六个数据集上评估了我们的方法。评估结果显示，我们的方法可以实现高达 0.85 的精确度得分，但召回率和 F1 得分较低。我们的方法可以快速构建基于规则的信息抽取系统，并构建注释数据集以训练基于机器学习和深度学习的分类器。