Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning (2208.14565v2)

Abstract: We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space. Prior work predominantly approaches NER as sequence labeling or span classification. We instead frame NER as a representation learning problem that maximizes the similarity between the vector representations of an entity mention and its type. This makes it easy to handle nested and flat NER alike, and can better leverage noisy self-supervision signals. A major challenge to this bi-encoder formulation for NER lies in separating non-entity spans from entity mentions. Instead of explicitly labeling all non-entity spans as the same class $\texttt{Outside}$ ($\texttt{O}$) as in most prior methods, we introduce a novel dynamic thresholding loss. Experiments show that our method performs well in both supervised and distantly supervised settings, for nested and flat NER alike, establishing new state of the art across standard datasets in the general domain (e.g., ACE2004, ACE2005) and high-value verticals such as biomedicine (e.g., GENIA, NCBI, BC5CDR, JNLPBA). We release the code at github.com/microsoft/binder.

• The paper introduces a bi-encoder framework that employs contrastive learning to align text span representations with entity types.
• It innovatively uses dynamic thresholding loss to distinguish between non-entity spans and genuine entity mentions, addressing nested NER challenges.
• Experimental validation shows F1 improvements up to 2.9% on nested datasets and gains in distantly supervised settings, confirming its robustness.

Bi-Encoder Optimization for Named Entity Recognition through Contrastive Learning

This paper introduces a novel bi-encoder framework designed to enhance Named Entity Recognition (NER) capabilities through the application of contrastive learning. Unlike traditional sequence labeling or span classification approaches, this research frames NER as a representation learning problem, emphasizing the alignment of text spans and entity types within a shared vector space.

Key Methodological Strategies

The proposed bi-encoder model applies two separate encoders: one for text spans and one for entity types. The system projects these into a unified vector space, aiming to maximize the similarity between the representation of an entity mention and its corresponding type. This formulation provides a seamless handling of both nested and flat NER scenarios and is particularly effective in leveraging noisy self-supervised signals—an advantage over conventional methods that typically require explicit class labels.

A notable methodological innovation in this work is the introduction of a dynamic thresholding loss. This approach addresses the challenge of distinguishing non-entity spans from genuine entity mentions, diverging from the prevalent practice of labeling non-entities under a common 'Outside' (O) class. By integrating this with the standard contrastive loss, the authors propose a more nuanced framework that effectively caters to the varied representations encountered in NER tasks.

Experimental Validation

The efficacy of the bi-encoder framework is thoroughly validated across several datasets. In supervised settings, the paper reports significant improvements over established state-of-the-art methods across both nested (ACE2004, ACE2005, GENIA) and flat NER datasets (e.g., CoNLL2003). For instance, the paper documents an F1 score improvement of 2.4% to 2.9% for nested NER datasets such as ACE2004 and ACE2005, which underscores the robust performance of their model.

Furthermore, in distantly supervised environments, the framework maintains superior performance metrics despite the inherent challenges posed by noisy data labeling. This is demonstrated by a 1.5% increase in F1 score for the BC5CDR dataset, illustrating the framework's ability to handle suboptimal supervision levels effectively.

Implications and Future Directions

The implications of this research are multifaceted, with both practical and theoretical advancements. Practically, the successful application of bi-encoder and contrastive learning to NER tasks simplifies the model complexity while enhancing the ability to manage nested entities—a common challenge in various domains including biomedicine and general information extraction systems. Theoretically, this work opens avenues for further research into representation learning's role in improving NER performance, particularly under less-than-ideal training conditions.

Moving forward, the exploration of bi-encoder frameworks within zero-shot learning settings presents an intriguing research trajectory. Additionally, integrating more diverse datasets to further evaluate model robustness and generalizability could offer deeper insights into the dynamic adaptability of such predictive models in real-world applications. This work sets a solid foundation for future exploration into nuanced NER systems empowered by sophisticated learning approaches like contrastive learning.