Information extraction techniques, including named entity recognition (NER)
and relation extraction (RE), are crucial in many domains to support making
sense of vast amounts of unstructured text data by identifying and connecting
relevant information. Such techniques can assist researchers in extracting
valuable insights. In this paper, we introduce the Entity-aware Masking for
Biomedical Relation Extraction (EMBRE) method for biomedical relation
extraction, as applied in the context of the BioRED challenge Task 1, in which
human-annotated entities are provided as input. Specifically, we integrate
entity knowledge into a deep neural network by pretraining the backbone model
with an entity masking objective. We randomly mask named entities for each
instance and let the model identify the masked entity along with its type. In
this way, the model is capable of learning more specific knowledge and more
robust representations. Then, we utilize the pre-trained model as our backbone
to encode language representations and feed these representations into two
multilayer perceptron (MLPs) to predict the logits for relation and novelty,
respectively. The experimental results demonstrate that our proposed method can
improve the performances of entity pair, relation and novelty extraction over
our baseline.

我们介绍了一种应用于生物医学关系提取的 Entity-aware Masking for Biomedical Relation Extraction (EMBRE) 方法，该方法将实体知识整合到深度神经网络中，通过预训练模型的实体屏蔽目标使模型能够学习更具体的知识和更强大的表示，并利用该模型来编码语言表示并预测关系和新颖性的逻辑回归，实验结果显示我们的方法可以提高实体对、关系和新颖性提取的性能。

EMBRE: 生物医学关系提取的实体感知掩码

EMBRE: Entity-aware Masking for Biomedical Relation Extraction

Over the last decade, several regulatory bodies have started requiring the
disclosure of non-financial information from publicly listed companies, in
light of the investors' increasing attention to Environmental, Social, and
Governance (ESG) issues. Such information is publicly released in a variety of
non-structured and multi-modal documentation. Hence, it is not straightforward
to aggregate and consolidate such data in a cohesive framework to further
derive insights about sustainability practices across companies and markets.
Thus, it is natural to resort to Information Extraction (IE) techniques to
provide concise, informative and actionable data to the stakeholders. Moving
beyond traditional text processing techniques, in this work we leverage Large
Language Models (LLMs), along with prominent approaches such as Retrieved
Augmented Generation and in-context learning, to extract semantically
structured information from sustainability reports. We then adopt graph-based
representations to generate meaningful statistical, similarity and correlation
analyses concerning the obtained findings, highlighting the prominent
sustainability actions undertaken across industries and discussing emerging
similarity and disclosing patterns at company, sector and region levels.
Lastly, we investigate which factual aspects impact the most on companies' ESG
scores using our findings and other company information.

近十年来，随着投资者对环境、社会和公司治理（ESG）问题日益关注，一些监管机构开始要求上市公司披露非财务信息。本文利用大型语言模型和信息提取技术，从非结构化的多模态文档中提取语义结构化信息，并采用基于图的表示方法进行相关分析，揭示不同行业间的突出可持续行动和公司、部门以及区域层面的相似性和披露模式，最终通过研究发现和其他公司信息，探讨影响公司 ESG 评分最显著的实际因素。