An Anchor Learning Approach for Citation Field Learning

TL;DR

This paper introduces CIFAL, a novel anchor learning algorithm that enhances citation field segmentation from diverse and inconsistent citation data, significantly improving performance over existing methods.

Contribution

The paper presents CIFAL, a model-agnostic anchor learning approach that effectively captures citation patterns across styles, advancing citation field extraction techniques.

Findings

CIFAL outperforms state-of-the-art methods by 2.68% in F1-score.

The approach is robust across different citation styles and data sources.

Extensive analysis confirms CIFAL's effectiveness both quantitatively and qualitatively.

Abstract

Citation field learning is to segment a citation string into fields of interest such as author, title, and venue. Extracting such fields from citations is crucial for citation indexing, researcher profile analysis, etc. User-generated resources like academic homepages and Curriculum Vitae, provide rich citation field information. However, extracting fields from these resources is challenging due to inconsistent citation styles, incomplete sentence syntax, and insufficient training data. To address these challenges, we propose a novel algorithm, CIFAL (citation field learning by anchor learning), to boost the citation field learning performance. CIFAL leverages the anchor learning, which is model-agnostic for any Pre-trained Language Model, to help capture citation patterns from the data of different citation styles. The experiments demonstrate that CIFAL outperforms state-of-the-art methods in citation field learning, achieving a 2.68% improvement in field-level F1-scores. Extensive analysis of the results further confirms the effectiveness of CIFAL quantitatively and qualitatively.