Text simplification is one of the domains in Natural Language Processing
(NLP) that offers an opportunity to understand the text in a simplified manner
for exploration. However, it is always hard to understand and retrieve
knowledge from unstructured text, which is usually in the form of compound and
complex sentences. There are state-of-the-art neural network-based methods to
simplify the sentences for improved readability while replacing words with
plain English substitutes and summarising the sentences and paragraphs. In the
Knowledge Graph (KG) creation process from unstructured text, summarising long
sentences and substituting words is undesirable since this may lead to
information loss. However, KG creation from text requires the extraction of all
possible facts (triples) with the same mentions as in the text. In this work,
we propose a controlled simplification based on the factual information in a
sentence, i.e., triple. We present a classical syntactic dependency-based
approach to split and rephrase a compound and complex sentence into a set of
simplified sentences. This simplification process will retain the original
wording with a simple structure of possible domain facts in each sentence,
i.e., triples. The paper also introduces an algorithm to identify and measure a
sentence's syntactic complexity (SC), followed by reduction through a
controlled syntactic simplification process. Last, an experiment for a dataset
re-annotation is also conducted through GPT3; we aim to publish this refined
corpus as a resource. This work is accepted and presented in International
workshop on Learning with Knowledge Graphs (IWLKG) at WSDM-2023 Conference. The
code and data is available at www.github.com/sallmanm/SynSim.

本研究提出了一种基于事实信息的控制简化方法，使用句法依赖分析将复合和复杂句子拆分为简化句子的集合，并用统计方法度量了句子的句法复杂度，通过本方法，可在不失信息的情况下简化语言，用于知识图谱中三元组的提取与构建。

通过受控语法简化识别、测量和降低句法复杂性

Syntactic Complexity Identification, Measurement, and Reduction Through  Controlled Syntactic Simplification

We present an approach for recursively splitting and rephrasing complex
English sentences into a novel semantic hierarchy of simplified sentences, with
each of them presenting a more regular structure that may facilitate a wide
variety of artificial intelligence tasks, such as machine translation (MT) or
information extraction (IE). Using a set of hand-crafted transformation rules,
input sentences are recursively transformed into a two-layered hierarchical
representation in the form of core sentences and accompanying contexts that are
linked via rhetorical relations. In this way, the semantic relationship of the
decomposed constituents is preserved in the output, maintaining its
interpretability for downstream applications. Both a thorough manual analysis
and automatic evaluation across three datasets from two different domains
demonstrate that the proposed syntactic simplification approach outperforms the
state of the art in structural text simplification. Moreover, an extrinsic
evaluation shows that when applying our framework as a preprocessing step the
performance of state-of-the-art Open IE systems can be improved by up to 346%
in precision and 52% in recall. To enable reproducible research, all code is
provided online.

该研究提出了一种递归分割和重述复杂英语句子的方法，生成简化句子的新语义层级结构，可用于机器翻译或信息提取等人工智能任务，手动和自动评估都表明该方法在结构文本简化方面表现优于现有技术，并用于近似 346% 精度和 52% 召回率地改良现有句子提取系统的性能。