[Paper Review] Complex Relation Extraction: Challenges and Opportunities

Haiyun Jiang, Qiaoben Bao+4 more|arXiv (Cornell University)|Dec 9, 2020

Topic Modeling23 references4 citations

TL;DR

This paper provides a comprehensive survey of complex relation extraction (CRE) tasks, identifying key challenges and opportunities beyond traditional binary relation extraction. It categorizes and analyzes emerging tasks such as few-shot, document-level, cross-lingual, multi-modal, N-ary, multi-grained, conditional, nested, and overlapping relation extraction, highlighting their definitions, recent progress, core challenges, and future research directions.

ABSTRACT

Relation extraction aims to identify the target relations of entities in texts. Relation extraction is very important for knowledge base construction and text understanding. Traditional binary relation extraction, including supervised, semi-supervised and distant supervised ones, has been extensively studied and significant results are achieved. In recent years, many complex relation extraction tasks, i.e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice. However, there is no literature to fully investigate and summarize these complex relation extraction works so far. In this paper, we first report the recent progress in traditional simple binary relation extraction. Then we summarize the existing complex relation extraction tasks and present the definition, recent progress, challenges and opportunities for each task.

Motivation & Objective

• To systematically survey and categorize complex relation extraction (CRE) tasks that extend beyond traditional binary relation extraction.
• To identify and analyze the core challenges hindering progress in each CRE subtask, such as data scarcity, structural complexity, and condition modeling.
• To highlight under-explored research opportunities in CRE, particularly in few-shot, multi-modal, nested, overlapping, and conditional relation extraction.
• To provide a unified framework for understanding the evolution from simple binary RE to more complex, real-world applicable relation extraction tasks.
• To guide future research by summarizing recent progress, open problems, and potential transferability of methods from simple BiRE to complex CRE tasks.

Proposed method

• Categorizes complex relation extraction into nine distinct subtasks: few-shot, document-level, cross-lingual, multi-modal, N-ary, multi-grained, conditional, nested, and overlapping RE.
• Reviews existing approaches for each subtask, including pattern-based methods, neural networks (e.g., BERT, Transformers), and end-to-end models with copy mechanisms.
• Analyzes challenges such as complex dependencies, implicit references, structural ambiguity, and lack of annotated data using case studies and prior work.
• Proposes a taxonomy of CRE tasks based on semantic complexity, input modality, and structural constraints.
• Evaluates the feasibility of transferring techniques from supervised, semi-supervised, and distant supervised binary RE to complex CRE settings.
• Identifies key architectural and training challenges, such as handling overlapping entity pairs and modeling conditional constraints in free text.

Experimental results

Research questions

• RQ1 What are the main limitations of traditional binary relation extraction that necessitate the development of complex relation extraction tasks?
• RQ2 How do different complex relation extraction tasks—such as few-shot, document-level, cross-lingual, and multi-modal RE—differ in their definitions, challenges, and current methodological approaches?
• RQ3 What are the core technical and data-related challenges in conditional and nested relation extraction, and why are they difficult to address with existing models?
• RQ4 How does overlapping relation extraction differ from standard binary RE, and what are the implications of ignoring overlapping relations in knowledge graph construction?
• RQ5 To what extent can techniques from traditional binary relation extraction be adapted to complex relation extraction tasks?

Key findings

• Complex relation extraction tasks have emerged to address the limitations of traditional binary relation extraction, particularly in handling low-resource scenarios, multi-sentence contexts, and complex semantic structures.
• Few-shot relation extraction shows promise using metric learning and meta-learning, though data scarcity remains a major challenge.
• Document-level, cross-lingual, and multi-modal relation extraction are gaining traction but suffer from lack of large-scale annotated datasets and complex dependency modeling.
• N-ary and multi-grained relation extraction are critical for knowledge base construction and question answering, but current models often fail to capture full relational context.
• Conditional relation extraction is underdeveloped due to the lack of formalized condition representations and annotated data, despite its importance for temporal and spatial reasoning.
• Overlapping relation extraction is particularly challenging due to ambiguous entity boundaries and multiple relations per entity pair, though recent models like CopyR and hierarchical RL frameworks show initial success.