Qualitative causal relationships compactly express the direction, dependency,
temporal constraints, and monotonicity constraints of discrete or continuous
interactions in the world. In everyday or academic language, we may express
interactions between quantities (e.g., sleep decreases stress), between
discrete events or entities (e.g., a protein inhibits another protein's
transcription), or between intentional or functional factors (e.g., hospital
patients pray to relieve their pain). Extracting and representing these diverse
causal relations are critical for cognitive systems that operate in domains
spanning from scientific discovery to social science. This paper presents a
transformer-based NLP architecture that jointly extracts knowledge graphs
including (1) variables or factors described in language, (2) qualitative
causal relationships over these variables, (3) qualifiers and magnitudes that
constrain these causal relationships, and (4) word senses to localize each
extracted node within a large ontology. We do not claim that our
transformer-based architecture is itself a cognitive system; however, we
provide evidence of its accurate knowledge graph extraction in real-world
domains and the practicality of its resulting knowledge graphs for cognitive
systems that perform graph-based reasoning. We demonstrate this approach and
include promising results in two use cases, processing textual inputs from
academic publications, news articles, and social media.

本文介绍了基于 transformer 的 NLP 架构，该架构可以联合提取包括语言描述、定性因果关系、约束限定词、词义在内的知识图谱，以及说明其在真实世界中的准确性和实用性。

基于 Transformer 的方法：从非结构化文本到因果知识图谱

From Unstructured Text to Causal Knowledge Graphs: A Transformer-Based Approach

Qualitative causal relationships compactly express the direction, dependency,
temporal constraints, and monotonicity constraints of discrete or continuous
interactions in the world. In everyday or academic language, we may express
interactions between quantities (e.g., sleep decreases stress), between
discrete events or entities (e.g., a protein inhibits another protein's
transcription), or between intentional or functional factors (e.g., hospital
patients pray to relieve their pain). This paper presents a transformer-based
NLP architecture that jointly identifies and extracts (1) variables or factors
described in language, (2) qualitative causal relationships over these
variables, and (3) qualifiers and magnitudes that constrain these causal
relationships. We demonstrate this approach and include promising results from
in two use cases, processing textual inputs from academic publications, news
articles, and social media.

本文介绍了一种基于 Transformer 的 NLP 结构，可以联合识别和提取 (1) 语言描述的变量或因素，(2) 这些变量之间的定性因果关系，(3) 限制这些因果关系的修饰语和数量词，从而在学术出版物、新闻文章和社交媒体等多个用例中展示了这种方法并获得了有前途的结果。