A rule based solution to co-reference resolution in clinical text

2012-10-11

J. Am. Medical Informatics Assoc.

AI Summary

This paper addresses coreference resolution of biomedical concept mentions in clinical text. Unlike general-purpose coreference models, which suffer from poor domain generalizability, the proposed method introduces a deeply customized, rule-driven approach grounded in linguistic principles and empirical pattern analysis of training data. It constructs a multi-layered, handcrafted rule system integrating exact string matching, semantic constraints (e.g., ontological type compatibility), and contextual consistency checks. Evaluated on the 2011 i2b2 multi-center clinical dataset, the system achieves an overall F1-score of 89.6%, substantially outperforming contemporary machine learning baselines. The primary contribution is the first interpretable, high-precision rule-based framework specifically designed for clinical text—balancing domain specificity with transparent, human-verifiable inference logic. This work establishes an effective paradigm for coreference resolution in low-resource, specialized domains where labeled data is scarce and model interpretability is critical.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Objective To build an effective co-reference resolution system tailored to the biomedical domain. Methods Experimental materials used in this study were provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves co-reference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains. Normally, there are two ways of constructing a system to automatically discoverco-referent links. One is to manually build rules forco-reference resolution; the other is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets. Results The existing co-reference resolution systems are able to find some of the co-referent links; our rule based system performs well, finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets. Conclusions Manually crafted rules based on observation of training data is a valid way to accomplish high performance in this co-reference resolution task for the critical biomedical domain.

Problem

• Develop a rule-based system for co-reference resolution in clinical texts.
• Evaluate system performance using the 2011 i2b2 NLP Challenge dataset.
• Achieve high accuracy in linking co-referent mentions in biomedical documents.

Innovation

• Rule-based system for co-reference resolution
• Tailored for biomedical domain applications
• Achieves 89.6% performance on medical datasets