End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models (1912.13415v1)

Abstract: Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE & IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external NLP tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained LLM. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.

End-to-end Named Entity Recognition and Relation Extraction Using Pre-trained LLMs

The paper presents a comprehensive approach to tackle Named Entity Recognition (NER) and Relation Extraction (RE) using pre-trained LLMs. By proposing a neural model that integrates a large pre-trained LLM, specifically BERT, this work aims to circumvent the limitations of dependency on external NLP tools and provide a more versatile and efficient solution for these tasks.

Key Contributions and Methodology

1. End-to-End Joint Learning: This paper proposes a joint, end-to-end model for simultaneously detecting entities and their relations without relying on external parsing tools. Such a model design eliminates the error propagation commonly seen in pipeline systems where NER and RE are decoupled processes.
2. Integration of Pre-trained LLMs: Leveraging BERT, a powerful transformer-based model, this architecture facilitates rapid and efficient training. The model utilizes self-attention mechanisms, avoiding recurrent structures, increasing both training speed and scalability.
3. Performance Achievements: Across five datasets spanning domains such as general news, biomedical, and clinical texts, the proposed methodology demonstrates a match or surplus over existing state-of-the-art performance benchmarks. Notably, on the ADE dataset related to biomedical texts, the model achieves substantial improvements in performance.
4. Entity Pretraining: The paper introduces a refined entity pretraining technique, ameliorating performance during early training phases by strategically weighting the RE loss.
5. Biaffine Attention Mechanism: In line with previous research, a deep biaffine attention mechanism is incorporated to encode directional relations more effectively, further optimizing relation extraction metrics.

Implications and Future Directions

The results indicate the efficacy of using a transformer LLM such as BERT for joint NER and RE tasks. This not only enhances model robustness across disparate genres but also significantly streamlines the training process by negating the need for bespoke linguistic feature engineering.

With potential expansions in several directions, the paper lays a foundational base for future research. The applicability could extend to multilingual corpora given the multilinguistic adaptations of BERT. Further exploration into recognizing nested entities or inter-sentence relations would enhance the model's applicability in nuanced scenarios like comprehensive text mining for knowledge base augmentation. Fine-tuning such systems on domain-specific corpora using tailored weights could potentially boost specialized information retrieval systems.

The paper underscores a shift towards finer integration of pre-trained models, promising advances in extracting semantic relations in text, ultimately fostering broader implementation in applications ranging from QA systems to biomedical literature mining.