TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2010.13415v1)

Abstract: Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works show that joint learning can result in a noticeable performance gain. However, they usually involve sequential interrelated steps and suffer from the problem of exposure bias. At training time, they predict with the ground truth conditions while at inference it has to make extraction from scratch. This discrepancy leads to error accumulation. To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias. TPLinker formulates joint extraction as a token pair linking problem and introduces a novel handshaking tagging scheme that aligns the boundary tokens of entity pairs under each relation type. Experiment results show that TPLinker performs significantly better on overlapping and multiple relation extraction, and achieves state-of-the-art performance on two public datasets.

• The paper presents TPLinker, a novel one-stage joint extraction method that minimizes exposure bias by linking token pairs.
• It employs a handshaking tagging scheme to efficiently identify entity boundaries and establish subject-object relation pairs.
• Experiments on NYT and WebNLG datasets demonstrate significant F1 score improvements, achieving up to 14.0% gain over prior models.

TPLinker: A Novel Joint Extraction Method for Entities and Relations

The paper presents TPLinker, an innovative model designed for the joint extraction of entities and relations from unstructured text. This model addresses a significant challenge in information extraction: the ability to efficiently detect overlapping relations that share entities, while avoiding exposure bias—a common problem in sequential extraction models.

Traditional approaches frequently separate entity detection and relation classification into a pipeline of interdependent steps, which can lead to error propagation and cascading errors. In contrast, TPLinker offers a one-stage joint extraction method, effectively reducing exposure bias by eliminating the segregation between training and inference processes.

Methodology and Implementation

The authors introduce a token pair linking framework in TPLinker, which leverages a novel handshaking tagging scheme to link token pairs for joint extraction tasks. The approach devised handles the extraction as a threefold problem:

1. Identifying entity head-to-tail positions.
2. Connecting subject head to object head positions.
3. Bridging subject tail to object tail positions.

For each sentence, TPLinker processes these three link sequences to extract entities and relate them under specific types, forming a comprehensive and efficient joint extraction framework. This solution alleviates the issues of exposure bias found in other models by aligning entity and relation extraction performances, ensuring consistent behavior during both training and inference stages.

Performance Evaluation

Experiments conducted on the NYT and WebNLG datasets illustrate TPLinker's superiority over existing models, with substantial improvements in F1 scores, particularly in the field of overlapping and multi-relation extraction. Notably, TPLinker achieves a 2.3% improvement in F1 score on the NYT$^\star$ dataset and a remarkable 14.0% improvement on the NYT dataset, placing it at the forefront of state-of-the-art performance benchmarks.

Importantly, TPLinker's architecture does not compromise computational efficiency. Despite employing a comprehensive token pair linking method, TPLinker maintains competitive inference times and parameter counts, especially when contrasted with other BERT-based models like CasRel. The innovation in tagging and linking also allows TPLinker to handle complex extraction cases, such as nested entities and singular pair overlaps, more effectively than prior models.

Implications and Future Directions

The implications of TPLinker extend beyond mere performance improvements in specific datasets. By effectively eliminating exposure bias and introducing a consistent tagging mechanism, TPLinker sets a new benchmark for models addressing joint extraction tasks. Its architecture offers substantial robustness for future advancements, suggesting applicability across various domains requiring complex relational networks.

Additionally, the handshaking tagging scheme could see potential adaptations in more complex scenarios, including nested entity extraction and event extraction, providing a versatile tool for diverse information extraction challenges. The model may inspire future research to capitalize on the advantages of one-stage joint models and further refine extraction technologies, minimizing computational overheads while maximizing extraction fidelity.

Overall, TPLinker exemplifies a cohesive approach to entity and relation extraction, offering a transformative perspective on model design within natural language processing. It provides a promising blueprint for subsequent models to enhance efficiency and accuracy in the domain of AI-driven information extraction systems.